Triple
T34475954
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gareth Wiley |
E885033
|
entity |
| Predicate | producedFilmSetIn |
P197534
|
FINISHED |
| Object | London |
—
|
NE NERFINISHED |
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: London | Statement: [Gareth Wiley, producedFilmSetIn, London]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: producedFilmSetIn Context triple: [Gareth Wiley, producedFilmSetIn, London]
-
A.
hasFilmingAssociation
Indicates a relationship where an entity is connected to another through involvement in the same filming activity, production, or filming-related context.
-
B.
filmingLocationForAdaptation
Indicates the place where an adaptation (such as a film or TV version of a work) was shot or recorded.
-
C.
basedInFilm
Indicates that something (such as a character, event, or work) is situated, set, or primarily located within the context or universe of a particular film.
-
D.
livesInFilmSetting
Indicates that an entity resides or exists within the fictional world or setting depicted in a particular film.
-
E.
producedFilm
Indicates that one entity served as the producer (or production company) responsible for making or financing the creation of a particular film.
- 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_69f349c880408190ade571c471ab154a |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69fe991bca608190b524e419642f4243 |
completed | May 9, 2026, 2:16 a.m. |
| PD | Predicate disambiguation | batch_69fe979fc1c4819091fc48d63ea12063 |
completed | May 9, 2026, 2:10 a.m. |
| PDg | Predicate description generation | batch_69fe991abc6c81908edbb98d61c9ca73 |
completed | May 9, 2026, 2:16 a.m. |
Created at: May 1, 2026, 2:01 a.m.