Triple
T36085266
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States entertainment industry |
E1043757
|
entity |
| Predicate | keySubindustry |
P184565
|
FINISHED |
| Object | Hollywood film industry |
—
|
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 film industry | Statement: [United States entertainment industry, keySubindustry, Hollywood film industry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: keySubindustry Context triple: [United States entertainment industry, keySubindustry, Hollywood film industry]
-
A.
subsidiaryIndustry
Indicates that a subsidiary company operates within or is associated with a particular industry sector.
-
B.
keySubsidiary
Indicates that one organization is the primary or most important subsidiary within a group of related companies.
-
C.
hasGICSSubIndustry
Indicates that an entity is classified within a specific GICS (Global Industry Classification Standard) sub-industry category.
-
D.
ownerSector
Indicates the sector or industry category to which the owner of an entity belongs.
-
E.
containsIndustry
Indicates that one entity includes or encompasses a particular industry within its scope, structure, or operations.
- 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_69f76e3154908190a6f702671c2bea08 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7b35e32d481909ef0220e6f6ff4a8 |
completed | May 3, 2026, 8:43 p.m. |
| PD | Predicate disambiguation | batch_69f7b1bad2e88190963ab4ee5d4f2038 |
completed | May 3, 2026, 8:36 p.m. |
| PDg | Predicate description generation | batch_69f7b2c66054819083897e25edb65ba7 |
completed | May 3, 2026, 8:40 p.m. |
Created at: May 3, 2026, 4:08 p.m.