Triple
T17890994
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Texas hold 'em poker (in the 2006 film) |
E447313
|
entity |
| Predicate | genreFunction |
P129188
|
FINISHED |
| Object | to merge spy thriller with gambling drama |
—
|
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: to merge spy thriller with gambling drama | Statement: [Texas hold 'em poker (in the 2006 film), genreFunction, to merge spy thriller with gambling drama]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genreFunction Context triple: [Texas hold 'em poker (in the 2006 film), genreFunction, to merge spy thriller with gambling drama]
-
A.
genreWithin
Indicates that one genre is a subgenre or more specific category contained within another, broader genre.
-
B.
genreFeatures
Indicates that a particular genre is characterized or defined by certain features or attributes.
-
C.
genre
Indicates the artistic or thematic category to which a work (such as a book, film, or song) belongs.
-
D.
genreOfAssociatedPerson
Indicates that a particular genre is associated with a given person, such as an artist, author, or performer.
-
E.
genreSpecialty
Indicates that an entity specializes in or is particularly associated with a specific genre.
- 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_69d8b9f59bd48190a6fc925a855b8bac |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49d7948888190b59ddc7061d13e84 |
completed | April 19, 2026, 9:16 a.m. |
| PD | Predicate disambiguation | batch_69e3d8e9b77c8190bbfb508f28dfacfa |
completed | April 18, 2026, 7:18 p.m. |
| PDg | Predicate description generation | batch_69e3db7704588190a34a422421152173 |
completed | April 18, 2026, 7:28 p.m. |
Created at: April 10, 2026, 10:18 a.m.