Triple
T13180307
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berlina |
E313704
|
entity |
| Predicate | manufacturer |
P490
|
FINISHED |
| Object | Holden |
E2747
|
NE FINISHED |
How this triple was built (2 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: Holden | Statement: [Berlina, manufacturer, Holden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Holden Context triple: [Berlina, manufacturer, Holden]
-
A.
Holden
Holden is a small town in central Utah, United States, known for its rural character and proximity to Interstate 15.
-
B.
Holden
Holden is a suburban town in Worcester County, Massachusetts, known for its residential character and proximity to the city of Worcester.
-
C.
Holden
chosen
Holden was an Australian automobile manufacturer and marque owned by General Motors, known for producing popular locally designed cars before ceasing operations in the 21st century.
-
D.
Holden
Holden is a masculine given name of English origin, commonly used in English-speaking countries.
-
E.
Holden Ford
Holden Ford is a young, idealistic FBI agent in the TV series "Mindhunter" who pioneers criminal profiling by interviewing serial killers to understand their psychology.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d806ae1e08819090d95bfe1538cc17 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98c490ed081908ea54edb25c3de90 |
completed | April 10, 2026, 11:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f71f13a63c81909335c0a45c3f7eca |
completed | May 3, 2026, 10:10 a.m. |
Created at: April 9, 2026, 9:14 p.m.