Triple
T26976763
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mammootty |
E679478
|
entity |
| Predicate | hasStarredIn |
P5563
|
FINISHED |
| Object | over 400 films |
—
|
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: over 400 films | Statement: [Mammootty, hasStarredIn, over 400 films]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStarredIn Context triple: [Mammootty, hasStarredIn, over 400 films]
-
A.
starredActorWith
Indicates that one entity participated as an actor in a production together with another specified actor.
-
B.
hasFilmographyType
Indicates the type or category of film-related work associated with an entity (e.g., actor, director, producer) within its filmography.
-
C.
hasFilmCareer
Indicates that an entity has been professionally involved in the film industry as a career.
-
D.
featuredInFilmBy
Indicates that an entity is prominently included or showcased within a film that is created, directed, or produced by a specified person or organization.
-
E.
starredActor
chosen
Indicates that an actor performed a leading or significant role in a particular production or work.
- 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_69eeeb507a7081909d516e1fa08b7d29 |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f7a225a77c81908f8953ccfeb14336 |
completed | May 3, 2026, 7:29 p.m. |
| PD | Predicate disambiguation | batch_69f7a06d4f108190bae3ab9ae431d2c7 |
completed | May 3, 2026, 7:22 p.m. |
Created at: April 27, 2026, 6:43 a.m.