Triple
T4559254
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Few Good Men |
E120552
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Demi Moore |
E232497
|
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: Demi Moore | Statement: [A Few Good Men, portrayedBy, Demi Moore]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Demi Moore Context triple: [A Few Good Men, portrayedBy, Demi Moore]
-
A.
Demi Moore
chosen
Demi Moore is an American actress and film producer known for her leading roles in hit films of the 1980s and 1990s such as "Ghost," "A Few Good Men," and "G.I. Jane."
-
B.
Melanie Griffith
Melanie Griffith is an American actress known for her roles in films such as "Working Girl" and "Something Wild," and as a prominent figure in Hollywood since the 1970s.
-
C.
Jennifer Beals
Jennifer Beals is an American actress best known for her breakout role in the film "Flashdance" and her extensive work in both film and television.
-
D.
Lea Thompson
Lea Thompson is an American actress best known for her role as Lorraine Baines McFly in the Back to the Future film trilogy.
-
E.
Vivica A. Fox
Vivica A. Fox is an American actress and producer known for her roles in films such as Independence Day, Set It Off, and Kill Bill, as well as numerous television series.
- 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_69bd4636f1648190a701445c2fcd9c17 |
completed | March 20, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69bd5829cc34819086ad2ae58446502e |
completed | March 20, 2026, 2:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdc593eaf881908a9043366230b391 |
completed | March 20, 2026, 10:09 p.m. |
Created at: March 20, 2026, 1:09 p.m.