Triple
T23002237
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Polar (2019 film) |
E572664
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Anthony Grant |
—
|
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: Anthony Grant | Statement: [Polar (2019 film), starring, Anthony Grant]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anthony Grant Context triple: [Polar (2019 film), starring, Anthony Grant]
-
A.
Anthony Grant
chosen
Anthony Grant is an actor known for his role in the action-comedy film "Polar."
-
B.
Frank Haith
Frank Haith is an American college basketball coach best known for leading programs such as the Miami Hurricanes, Missouri Tigers, and Tulsa Golden Hurricane men's teams.
-
C.
Gregg Marshall
Gregg Marshall is an American college basketball coach best known for his long, successful tenure at Wichita State University, where he built the Shockers into a national contender.
-
D.
Dan McGuire
Dan McGuire is best known as the husband of American artist Margaret Keane, whose distinctive "big eyes" paintings gained widespread fame.
-
E.
Mark Turgeon
Mark Turgeon is an American college basketball coach best known for leading the University of Maryland men's basketball program through much of the 2010s and early 2020s.
- 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_69e245b6a3ac81908087599eefe3e365 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18353d05481909abacb48a14ef21e |
completed | April 29, 2026, 4:04 a.m. |
Created at: April 17, 2026, 3:50 p.m.