Triple
T18380093
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amy Prentiss |
E446418
|
entity |
| Predicate | televisionFilmCount |
P96026
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [Amy Prentiss, televisionFilmCount, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: televisionFilmCount Context triple: [Amy Prentiss, televisionFilmCount, 3]
-
A.
numberOfTVFilms
chosen
Indicates the total count of television films associated with a given entity.
-
B.
televisionFilm
Indicates that the subject is a television film (a movie produced for or originally distributed via television).
-
C.
televisionFilmAdaptation
Indicates that a television film is an adaptation of another work, such as a book, play, or earlier production.
-
D.
filmAdaptationCount
Indicates the number of film adaptations that have been made from a given source work or material.
-
E.
televisionUniverse
Indicates that two entities exist within the same fictional television continuity or shared TV universe.
- 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_69d8b9f370b88190b1e5081c2c238e7f |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e5179aa328819097f5ed8193cfa401 |
completed | April 19, 2026, 5:57 p.m. |
| PD | Predicate disambiguation | batch_69e44ff1f92c8190afbb8e85d12bf2a9 |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:45 a.m.