Triple
T12030148
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old Tucson Studios |
E286382
|
entity |
| Predicate | hasFilmSetType |
P102864
|
FINISHED |
| Object | frontier town |
—
|
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: frontier town | Statement: [Old Tucson Studios, hasFilmSetType, frontier town]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFilmSetType Context triple: [Old Tucson Studios, hasFilmSetType, frontier town]
-
A.
hasFilmStyle
Indicates that a film exhibits or is characterized by a particular cinematic style or aesthetic approach.
-
B.
hasFilmColorType
Indicates that a film is associated with a particular color process or color classification (e.g., color, black-and-white).
-
C.
hasTheatricalForm
Indicates that something is associated with or presented in a particular theatrical form or style.
-
D.
hasInteractiveFilm
Indicates that an entity is associated with, offers, or features an interactive film experience.
-
E.
usesFilmFormat
Indicates that one entity employs or is recorded in a particular film format associated with the other entity.
- 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_69d6ab4669e48190b59246358b0383ab |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9100b4ca8819084845ca4c13e34ce |
completed | April 10, 2026, 2:58 p.m. |
| PD | Predicate disambiguation | batch_69d902b6ebbc8190b13c44a61c6f81b9 |
completed | April 10, 2026, 2:01 p.m. |
| PDg | Predicate description generation | batch_69d91006e14081909838412df082f794 |
completed | April 10, 2026, 2:58 p.m. |
Created at: April 8, 2026, 9:47 p.m.