Triple
T5215180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bad Religion |
E117731
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Generator
"Generator" is a 1992 punk rock album by Bad Religion that marked a darker, more experimental turn in the band's melodic hardcore sound.
|
E503802
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Generator | Statement: [Bad Religion, notableWork, Generator]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Generator Context triple: [Bad Religion, notableWork, Generator]
-
A.
Gerar
Gerar is an ancient Philistine city mentioned in the Hebrew Bible, associated with the patriarchs Abraham and Isaac in the region of the Negev.
-
B.
ExampleGen
ExampleGen is a TensorFlow Extended (TFX) component responsible for ingesting and converting raw data into standardized examples for machine learning pipelines.
-
C.
Generation
Generation is a youth-focused program of the Berlin International Film Festival that showcases films for and about children and teenagers.
-
D.
Generator Entertainment
Generator Entertainment is a television production company known for its involvement in high-profile series such as the first season of "Game of Thrones."
-
E.
SchemaGen
SchemaGen is a TensorFlow Extended (TFX) component that automatically infers and generates data schemas by analyzing example datasets for use in machine learning pipelines.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Generator Triple: [Bad Religion, notableWork, Generator]
Generated description
"Generator" is a 1992 punk rock album by Bad Religion that marked a darker, more experimental turn in the band's melodic hardcore sound.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Generator Target entity description: "Generator" is a 1992 punk rock album by Bad Religion that marked a darker, more experimental turn in the band's melodic hardcore sound.
-
A.
Gerar
Gerar is an ancient Philistine city mentioned in the Hebrew Bible, associated with the patriarchs Abraham and Isaac in the region of the Negev.
-
B.
ExampleGen
ExampleGen is a TensorFlow Extended (TFX) component responsible for ingesting and converting raw data into standardized examples for machine learning pipelines.
-
C.
Generation
Generation is a youth-focused program of the Berlin International Film Festival that showcases films for and about children and teenagers.
-
D.
Generator Entertainment
Generator Entertainment is a television production company known for its involvement in high-profile series such as the first season of "Game of Thrones."
-
E.
SchemaGen
SchemaGen is a TensorFlow Extended (TFX) component that automatically infers and generates data schemas by analyzing example datasets for use in machine learning pipelines.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69bd4464ba3c8190bc16b2ebbe42ddb0 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7a928dfc8190971a9e28d5c10446 |
completed | March 20, 2026, 4:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beefe325988190b35e3502f147c9c2 |
completed | March 21, 2026, 7:22 p.m. |
| NEDg | Description generation | batch_69bef0b2b6448190be1c465738be741b |
completed | March 21, 2026, 7:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bef121817c8190aebd27ee34c0a419 |
completed | March 21, 2026, 7:27 p.m. |
Created at: March 20, 2026, 1:48 p.m.