Triple
T31103161
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monsters |
E792726
|
entity |
| Predicate | filmingRegions |
P171148
|
FINISHED |
| Object | Central America |
—
|
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: Central America | Statement: [Monsters, filmingRegions, Central America]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmingRegions Context triple: [Monsters, filmingRegions, Central America]
-
A.
primaryFilmingRegions
Indicates the main geographic areas where the filming or production of a work primarily took place.
-
B.
countryOfFilming
Indicates the country where the filming or production of a work physically took place.
-
C.
coProductionRegions
Indicates the regions or countries that jointly participated in producing a work (e.g., a film or TV show) as co-producers.
-
D.
filmingCountry
Indicates the country where the filming or primary production of a work took place.
-
E.
cinemaRegion
Indicates that a cinema is located within, or associated with, a particular geographic or administrative region.
- 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_69f224cfd5d881908ec6447bc321cd58 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f69c6e0b888190a417ae712c42db0d |
completed | May 3, 2026, 12:53 a.m. |
| PD | Predicate disambiguation | batch_69f69665cd9c819088c388fc82fec42e |
completed | May 3, 2026, 12:27 a.m. |
| PDg | Predicate description generation | batch_69f69c2127088190ae92c72461576d3b |
completed | May 3, 2026, 12:51 a.m. |
Created at: April 29, 2026, 9:03 p.m.