Triple
T2900756
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Big |
E62646
|
entity |
| Predicate | leadActorBreakthrough |
P42600
|
FINISHED |
| Object | yes for Tom Hanks |
—
|
LITERAL 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: yes for Tom Hanks | Statement: [Big, leadActorBreakthrough, yes for Tom Hanks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leadActorBreakthrough Context triple: [Big, leadActorBreakthrough, yes for Tom Hanks]
-
A.
leadActorAwarded
Indicates that the person in the lead actor role has received an award for their performance.
-
B.
leadActress
Indicates that the subject is the primary female performer in the specified film, show, or production.
-
C.
starredActor
Indicates that an actor performed a leading or significant role in a particular production or work.
-
D.
notableStar
Indicates that the subject is a star (or stellar object) that is distinguished or noteworthy in some significant way, such as brightness, fame, or scientific interest, relative to other stars.
-
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. chosen
Provenance (4 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_69ab4c3e070c8190b78d3d2c005876dd |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abe0b081308190af8875151fb11c4e |
completed | March 7, 2026, 8:24 a.m. |
| PD | Predicate disambiguation | batch_69abdd19bac881908f047d616aca8438 |
completed | March 7, 2026, 8:08 a.m. |
| PDg | Predicate description generation | batch_69abdd96670c8190b727f9ac27dadf67 |
completed | March 7, 2026, 8:11 a.m. |
Created at: March 6, 2026, 10:10 p.m.