Triple
T24888076
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Preston Meyers |
E622912
|
entity |
| Predicate | spendsFilmTryingTo |
P157443
|
FINISHED |
| Object | confess his feelings to Amanda Beckett |
—
|
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: confess his feelings to Amanda Beckett | Statement: [Preston Meyers, spendsFilmTryingTo, confess his feelings to Amanda Beckett]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spendsFilmTryingTo Context triple: [Preston Meyers, spendsFilmTryingTo, confess his feelings to Amanda Beckett]
-
A.
statusDuringFilm
Indicates that a particular status or condition holds for an entity during the time span in which a specified film takes place or is being made.
-
B.
endedWithFilm
Indicates that an event, series, or process concluded with the showing, release, or occurrence of a particular film.
-
C.
showedFilm
Indicates that one entity presented or screened a film for another entity or audience.
-
D.
hasTypeOfUseInFilm
Indicates that something is associated with a specific manner or category of use within the context of a film.
-
E.
worksForAtStartOfFilm
Indicates that one entity is employed by or working for another entity at the beginning of the film's narrative.
- 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_69e2fac4aa848190b3446a3922cec150 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f43043512481909501a3979cac9947 |
completed | May 1, 2026, 4:46 a.m. |
| PD | Predicate disambiguation | batch_69f420fd375c81908ea4a4e60b76ee8f |
completed | May 1, 2026, 3:41 a.m. |
| PDg | Predicate description generation | batch_69f4303fad6c8190844f069164f0904d |
completed | May 1, 2026, 4:46 a.m. |
Created at: April 18, 2026, 5:25 a.m.