Triple
T33120805
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Iver |
E847589
|
entity |
| Predicate | hasFilmStudioNearby |
P177094
|
FINISHED |
| Object | Pinewood Studios |
—
|
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: Pinewood Studios | Statement: [Iver, hasFilmStudioNearby, Pinewood Studios]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFilmStudioNearby Context triple: [Iver, hasFilmStudioNearby, Pinewood Studios]
-
A.
hasNearbyFilmStudio
chosen
Indicates that one entity is located close to, or in the vicinity of, a film studio associated with another entity.
-
B.
hasNeighboringStudios
Indicates that one studio is located adjacent to or in close proximity to another studio.
-
C.
hasStudiosIn
Indicates that an entity operates or maintains studio facilities located in a specified place or region.
-
D.
hasStudio
Indicates that an entity (such as a film, game, or production) is associated with or produced by a particular studio.
-
E.
hasMovieTheater
Indicates that one entity possesses, contains, or includes a movie theater as part of its facilities or attributes.
- 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_69f3495751a081909850af5843da40dc |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69ff0b6bc4a88190bf1d38c6ea26bcdc |
completed | May 9, 2026, 10:24 a.m. |
| PD | Predicate disambiguation | batch_69ff082a22f4819095ded971dbd8ea7b |
completed | May 9, 2026, 10:10 a.m. |
Created at: May 1, 2026, 1:27 a.m.