Triple
T14943905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miss Congeniality |
E372602
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object | Stan Fields |
E548389
|
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: Stan Fields | Statement: [Miss Congeniality, character, Stan Fields]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stan Fields Context triple: [Miss Congeniality, character, Stan Fields]
-
A.
Stan Fields
chosen
Stan Fields is a fictional beauty pageant host and commentator featured in the comedy film series "Miss Congeniality."
-
B.
Jack Fields
Jack Fields was the first husband of American actress and television producer Whitney Blake.
-
C.
Ron Field
Ron Field was an American choreographer and director best known for his innovative work on Broadway musicals in the mid-20th century.
-
D.
Joseph Fields
Joseph Fields was an American playwright, screenwriter, and producer known for his work on Broadway and in Hollywood during the mid-20th century.
-
E.
Adam Fields
Adam Fields is a film producer known for his work on various Hollywood movies, including the comedy "The Wedding Ringer."
- 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_69d85cc9da0c81908d583ca3f63a3908 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded68d20048190a403af85fe43dede |
completed | April 15, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe968d71dc81909b76551f9cd9ebab |
completed | May 9, 2026, 2:06 a.m. |
Created at: April 10, 2026, 2:38 a.m.