Triple
T12932016
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paul Weitz |
E309406
|
entity |
| Predicate | workedWith |
P398
|
FINISHED |
| Object | Dennis Quaid |
E330658
|
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: Dennis Quaid | Statement: [Paul Weitz, workedWith, Dennis Quaid]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dennis Quaid Context triple: [Paul Weitz, workedWith, Dennis Quaid]
-
A.
Dennis Quaid
chosen
Dennis Quaid is an American actor known for his versatile performances in films ranging from dramas like "The Right Stuff" and "Far from Heaven" to popular comedies and action movies.
-
B.
James Woods
James Woods is an American actor known for his intense performances in film and television, including acclaimed roles in movies such as "Salvador," "Videodrome," and "Casino."
-
C.
Kevin Judd
Kevin Judd is a musician best known as a member of the American rock band Ultra Q.
-
D.
Richard Gere
Richard Gere is an American actor known for his leading roles in films such as "American Gigolo," "An Officer and a Gentleman," and "Pretty Woman."
-
E.
James Gardner
James Gardner is a relatively common personal name shared by multiple notable individuals across fields such as politics, the arts, and academia.
- 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_69d7bdfa933c8190b5a27aa4a08a19b7 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97dc53060819090a126f15428e411 |
completed | April 10, 2026, 10:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6b8d58a0c8190b96252f04fdf1256 |
completed | May 3, 2026, 2:54 a.m. |
Created at: April 9, 2026, 5:42 p.m.