Triple
T37352669
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kleinfeld Bridal |
E927366
|
entity |
| Predicate | longtimeFilmingLocationOf |
P31730
|
FINISHED |
| Object | Say Yes to the Dress |
—
|
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: Say Yes to the Dress | Statement: [Kleinfeld Bridal, longtimeFilmingLocationOf, Say Yes to the Dress]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: longtimeFilmingLocationOf Context triple: [Kleinfeld Bridal, longtimeFilmingLocationOf, Say Yes to the Dress]
-
A.
formerFilmingLocation
Indicates that a place was once used as a filming location for a work but is no longer used for that purpose.
-
B.
notableFilmingLocation
chosen
Indicates that a place served as a significant or well-known location where a film or television production was shot.
-
C.
filmingLocationContext
Indicates the contextual relationship specifying where the filming of an event, scene, or production took place.
-
D.
filmingLocationPattern
Indicates the typical or recurring geographic pattern of locations where filming for a production takes place.
-
E.
filmingLocationCity
Indicates the city where the filming or recording of a work took place.
- 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_69f76eb5e034819088e53ab5b7909a68 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fb8c38a9688190be524246f5682107 |
completed | May 6, 2026, 6:45 p.m. |
| PD | Predicate disambiguation | batch_69fb5a9c6e0481908565bd849e869b24 |
completed | May 6, 2026, 3:13 p.m. |
Created at: May 3, 2026, 4:16 p.m.