Triple
T7111495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EPA Clean Air Scientific Advisory Committee |
E165716
|
entity |
| Predicate | memberExpertise |
P18508
|
FINISHED |
| Object | air quality science |
—
|
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: air quality science | Statement: [EPA Clean Air Scientific Advisory Committee, memberExpertise, air quality science]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: memberExpertise Context triple: [EPA Clean Air Scientific Advisory Committee, memberExpertise, air quality science]
-
A.
sharesCompetencesWith
Indicates that two entities possess and can apply the same or overlapping set of skills, abilities, or areas of expertise.
-
B.
skillSet
Indicates that an entity possesses or is associated with a particular collection of skills or competencies.
-
C.
competenceArea
chosen
Indicates that one entity has a particular domain, field, or area in which it possesses competence, expertise, or responsibility.
-
D.
hasSpecialist
Indicates that one entity is associated with or assigned to a specialist entity that provides expert support, service, or oversight for it.
-
E.
memberProfession
Indicates that a member or individual holds or practices a particular profession or occupation.
- 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_69c6888120f081908f8f01b201dc4a4c |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e5ecd4488190bf19e42de55da98b |
completed | March 27, 2026, 8:17 p.m. |
| PD | Predicate disambiguation | batch_69c6e1c4f9788190830288d00cc37026 |
completed | March 27, 2026, 8 p.m. |
Created at: March 27, 2026, 2:43 p.m.