Triple
T31925632
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Larry Aldrich |
E815098
|
entity |
| Predicate | mainBeneficiarySector |
P32550
|
FINISHED |
| Object | museums |
—
|
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: museums | Statement: [Larry Aldrich, mainBeneficiarySector, museums]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainBeneficiarySector Context triple: [Larry Aldrich, mainBeneficiarySector, museums]
-
A.
sectorBenefited
chosen
Indicates that a particular sector gains advantage, support, or positive impact from a given action, policy, resource, or entity.
-
B.
ownerSector
Indicates the sector or industry category to which the owner of an entity belongs.
-
C.
typicalConstituentSector
Indicates that something is a usual or characteristic sector that forms part of a larger whole or system.
-
D.
economicSectors
Indicates a relationship that associates entities with the economic sectors or industries in which they operate or to which they belong.
-
E.
primaryBeneficiaries
Indicates which entities are the main recipients or advantaged parties resulting from a particular action, resource, or arrangement.
- 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_69f348f1df848190851bbfb988da3414 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6b21e7e088190832a3db585daea1c |
completed | May 3, 2026, 2:25 a.m. |
| PD | Predicate disambiguation | batch_69f6b14faf608190a25b977c0740729c |
completed | May 3, 2026, 2:22 a.m. |
Created at: May 1, 2026, 12:03 a.m.