Triple
T14877597
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bret Easton Ellis |
E349906
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
White
"White" is a 2019 non-fiction essay collection by Bret Easton Ellis in which he offers provocative cultural criticism and personal reflections on contemporary politics, media, and identity.
|
E1126086
|
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: White | Statement: [Bret Easton Ellis, notableWork, White]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: White Context triple: [Bret Easton Ellis, notableWork, White]
-
A.
White
White is a common English surname borne by numerous notable individuals across politics, arts, sciences, and other fields.
-
B.
White
White is a small town in Bartow County, Georgia, known for its rural character and proximity to Cartersville in the northwestern part of the state.
-
C.
Blue
Blue is the internal codename Microsoft used during development of the Windows 8.1 operating system update.
-
D.
Blue
Blue is the entry-level tier in Uber's driver rewards program, offering basic benefits and recognition for new or less frequent drivers.
-
E.
Blue
"Blue" is a film featuring actress Wanda De Jesus in a significant role.
- 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: White Triple: [Bret Easton Ellis, notableWork, White]
Generated description
"White" is a 2019 non-fiction essay collection by Bret Easton Ellis in which he offers provocative cultural criticism and personal reflections on contemporary politics, media, and identity.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: White Target entity description: "White" is a 2019 non-fiction essay collection by Bret Easton Ellis in which he offers provocative cultural criticism and personal reflections on contemporary politics, media, and identity.
-
A.
White
White is a common English surname borne by numerous notable individuals across politics, arts, sciences, and other fields.
-
B.
White
White is a small town in Bartow County, Georgia, known for its rural character and proximity to Cartersville in the northwestern part of the state.
-
C.
Blue
Blue is the internal codename Microsoft used during development of the Windows 8.1 operating system update.
-
D.
Blue
Blue is the entry-level tier in Uber's driver rewards program, offering basic benefits and recognition for new or less frequent drivers.
-
E.
Blue
"Blue" is a film featuring actress Wanda De Jesus in a significant role.
- 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_69d822ee4f408190b6ac3b2fa434f0df |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded5e4e4448190a8796573bc6d1069 |
completed | April 15, 2026, 12:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe6b54ad7c819082575245da07e358 |
completed | May 8, 2026, 11:01 p.m. |
| NEDg | Description generation | batch_69fe6be21f148190bec0e5adfcc0a91a |
completed | May 8, 2026, 11:04 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe6c6ebe4881909334d772e45403f6 |
completed | May 8, 2026, 11:06 p.m. |
Created at: April 10, 2026, 1:55 a.m.