Triple
T12877332
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Face/Off |
E308001
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object | Mike Werb |
E524243
|
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: Mike Werb | Statement: [Face/Off, writer, Mike Werb]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mike Werb Context triple: [Face/Off, writer, Mike Werb]
-
A.
Mike Werb
chosen
Mike Werb is an American screenwriter best known for co-writing high-concept Hollywood films such as "Face/Off" and "The Mask."
-
B.
Brian Yablonski
Brian Yablonski is a conservation-focused public policy leader and writer known for his work on wildlife, land stewardship, and free-market environmentalism.
-
C.
Mike Nussbaum
Mike Nussbaum is an American actor and director known for his character roles in film, television, and theater, including work in David Mamet projects such as "House of Games."
-
D.
Phil DeVoss
Phil DeVoss is a fictional character from the romantic comedy-drama film "Elizabethtown," which explores themes of family, failure, and self-discovery.
-
E.
Kevin Weisman
Kevin Weisman is an American character actor best known for his role as Marshall Flinkman on the television series "Alias" and for numerous appearances in film and television comedies and dramas.
- 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_69d7bdf69bc48190af6c2621f28ca351 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d970fa8474819086a8af3c90f3ca84 |
completed | April 10, 2026, 9:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6b8ccee708190bb4caa604386e3a3 |
completed | May 3, 2026, 2:54 a.m. |
Created at: April 9, 2026, 5:38 p.m.