Triple
T9117230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pretty Woman |
E218750
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Richard Gere |
E103439
|
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: Richard Gere | Statement: [Pretty Woman, starring, Richard Gere]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Richard Gere Context triple: [Pretty Woman, starring, Richard Gere]
-
A.
Richard Gere
chosen
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."
-
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.
Scott Anthony Redford
Scott Anthony Redford is one of the children of acclaimed American actor and filmmaker Robert Redford.
-
D.
Michael Douglas
Michael Douglas is an acclaimed American actor and producer known for films like "Wall Street" and "Fatal Attraction," who has also been recognized for his humanitarian and peace-promoting work.
-
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_69ca83dc94ac8190b9ef42684d36ff39 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca8a5e2ac8190b602ef0c77deb2fa |
completed | April 1, 2026, 5:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0307299ec8190acade4f388642e23 |
completed | April 3, 2026, 9:26 p.m. |
Created at: March 30, 2026, 7:17 p.m.