Triple
T33139927
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ignacia Allamand |
E848113
|
entity |
| Predicate | notableGenreParticipation |
P146635
|
FINISHED |
| Object | Latin American romantic comedies |
—
|
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: Latin American romantic comedies | Statement: [Ignacia Allamand, notableGenreParticipation, Latin American romantic comedies]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableGenreParticipation Context triple: [Ignacia Allamand, notableGenreParticipation, Latin American romantic comedies]
-
A.
notableGenreAsFilmmaker
Indicates the film genre for which a filmmaker is particularly recognized or distinguished.
-
B.
notableGenreOfFilm
Indicates that a particular genre is especially characteristic, prominent, or noteworthy for a given film.
-
C.
notableGenreCollaboration
Indicates a collaborative relationship between entities that is particularly significant or well-known within a specific genre.
-
D.
notableWorkGenre
Indicates that a particular work is recognized as notable for an entity and specifies the genre to which that work belongs.
-
E.
hasGenreOfWorkItAppearsIn
chosen
Indicates that an entity is associated with the genre of the work in which it appears.
- 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_69f3495961d88190b16ea542c2c5f825 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a037c894b488190bcbec2eccaff4a01 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379f338b881908e5593e45d764f4d |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 1:28 a.m.