Triple
T33532117
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Big Daddy |
E858822
|
entity |
| Predicate | realNameInFilm |
P9233
|
FINISHED |
| Object | Damon Macready |
—
|
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: Damon Macready | Statement: [Big Daddy, realNameInFilm, Damon Macready]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: realNameInFilm Context triple: [Big Daddy, realNameInFilm, Damon Macready]
-
A.
givenNameInFilm
Indicates that a person is referred to by a particular given (first) name within the context of a specific film.
-
B.
realName
chosen
Indicates that one entity is the actual, full, or birth name of another entity, which may be known by an alias, nickname, or alternate identity.
-
C.
fullNameInFilmCredits
Indicates that a person’s complete legal or professional name is used in the official credits of a film.
-
D.
hasProtagonistNameInFilmAdaptation
Indicates that a specific name is used for the story’s main character in a particular film adaptation.
-
E.
madeFamousByFilm
Indicates that something became widely known or gained significant public recognition as a result of being featured in a film.
- F. None of above.
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_69f34978caf4819083f90eba4944d8e8 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6f8164698819090c1b471f1caa4c6 |
completed | May 3, 2026, 7:24 a.m. |
| PD | Predicate disambiguation | batch_69f6f6619404819084662aef1238261c |
completed | May 3, 2026, 7:16 a.m. |
Created at: May 1, 2026, 1:39 a.m.