Triple
T13244773
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Steve David |
E315371
|
entity |
| Predicate | fullName |
P16
|
FINISHED |
| Object | Steve David |
E315371
|
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: Steve David | Statement: [Steve David, fullName, Steve David]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Steve David Context triple: [Steve David, fullName, Steve David]
-
A.
Steve David
chosen
Steve David was a prolific Trinidadian forward best known for his goal-scoring exploits in the North American Soccer League during the 1970s.
-
B.
Steve Dennis
Steve Dennis is a writer best known for co-authoring the memoir "Fallin’ Up: My Story."
-
C.
Phil DeVoss
Phil DeVoss is a fictional character from the romantic comedy-drama film "Elizabethtown," which explores themes of family, failure, and self-discovery.
-
D.
Jeff Danna
Jeff Danna is a Canadian film composer known for his scores for movies such as The Boondock Saints and various animated and dramatic films.
-
E.
Steve Judd
Steve Judd is the aging, principled former lawman at the heart of the Western film "Ride the High Country," whose moral integrity drives the story’s central conflict.
- 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_69d806b1072881909e46bd212259c5f0 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98d5b06148190a44e698bbe5cd529 |
completed | April 10, 2026, 11:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6ff3600388190a7f61370f54d5e63 |
completed | May 3, 2026, 7:54 a.m. |
Created at: April 9, 2026, 9:23 p.m.