Triple
T672170
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scientific and Technical Awards |
E12994
|
entity |
| Predicate | beneficiaryCommunity |
P7910
|
FINISHED |
| Object | film industry |
—
|
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: film industry | Statement: [Scientific and Technical Awards, beneficiaryCommunity, film industry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: beneficiaryCommunity Context triple: [Scientific and Technical Awards, beneficiaryCommunity, film industry]
-
A.
beneficiaries
chosen
Indicates that certain entities receive advantages, profits, or positive outcomes from an action, event, or arrangement.
-
B.
philanthropicBeneficiary
Indicates that one entity is the recipient or target of another entity’s philanthropic giving or charitable support.
-
C.
beneficiaryRegion
Indicates the region that receives the benefit, advantage, or positive impact resulting from an action, resource, or arrangement.
-
D.
supportsCommunity
Indicates that one entity provides assistance, resources, or encouragement that benefits a community or group.
-
E.
beneficiaryCountry
Indicates that one country is the recipient or beneficiary of aid, resources, or advantages provided in a given context.
- 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_69a493355dec819098d4244b2fa34885 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a1b3682c8190a9b9a454480c3446 |
completed | March 1, 2026, 8:29 p.m. |
| PD | Predicate disambiguation | batch_69a49d1a16c48190af89e3b078a4957e |
completed | March 1, 2026, 8:10 p.m. |
Created at: March 1, 2026, 7:36 p.m.