Triple
T20035166
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | David Brian |
E497236
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | David Brian |
—
|
NE NERFINISHED |
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: David Brian | Statement: [David Brian, name, David Brian]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: David Brian Context triple: [David Brian, name, David Brian]
-
A.
David Brian
chosen
David Brian was an American film and television actor known for his roles in mid-20th-century Hollywood dramas and crime films.
-
B.
David Daniel
David Daniel is a cinematographer best known for his work on the dance film "You Got Served."
-
C.
Dan Talbot
Dan Talbot was an influential American film distributor and exhibitor known for championing foreign and independent cinema in the United States.
-
D.
David Dawson
David Dawson is a British actor known for his work in television, film, and theatre, including a prominent role in the romantic drama film "My Policeman."
-
E.
David Bruce
David Bruce was an American film and television actor active primarily in the 1940s and 1950s, known for supporting roles in Hollywood productions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da627278c88190babe4297a9df1236 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e662e76f8481909c006921cbbfd060 |
completed | April 20, 2026, 5:31 p.m. |
Created at: April 11, 2026, 3:36 p.m.