Triple
T35624011
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metrocolor |
E1029397
|
entity |
| Predicate | geographicLocationOfUse |
P150084
|
FINISHED |
| Object | Hollywood |
—
|
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: Hollywood | Statement: [Metrocolor, geographicLocationOfUse, Hollywood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: geographicLocationOfUse Context triple: [Metrocolor, geographicLocationOfUse, Hollywood]
-
A.
geographicalUsage
chosen
Indicates that something is used, applied, or occurs within a particular geographic area or region.
-
B.
usedInCountryOrRegion
Indicates that something (such as an item, concept, or practice) is utilized or applied within a specified country or region.
-
C.
geographicReception
Indicates the geographic area or location where something (such as a work, message, or signal) is received, experienced, or has effect.
-
D.
hasTypicalUsageRegion
Indicates that something is most commonly or characteristically used within a particular geographic region.
-
E.
geographicDeployment
Indicates how something is distributed, implemented, or present across different geographic locations or regions.
- 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_69f76e0709408190bbe322bf1707ef6b |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fce7671f108190bf3ebf54339068b5 |
completed | May 7, 2026, 7:26 p.m. |
| PD | Predicate disambiguation | batch_69fce5b5a84c81908ac1b5b9f08d48d0 |
completed | May 7, 2026, 7:19 p.m. |
Created at: May 3, 2026, 4:05 p.m.