Triple
T27902795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ira A. Fulton |
E705680
|
entity |
| Predicate | primaryAreaOfImpact |
P19488
|
FINISHED |
| Object | American higher education |
—
|
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: American higher education | Statement: [Ira A. Fulton, primaryAreaOfImpact, American higher education]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryAreaOfImpact Context triple: [Ira A. Fulton, primaryAreaOfImpact, American higher education]
-
A.
primaryArea
chosen
Indicates that one entity is the main or most important area, domain, or field associated with another entity.
-
B.
impactCategory
Indicates the type or domain of effect that one entity or action has on another, classifying the nature of its impact.
-
C.
primaryInfluence
Indicates that one entity serves as the main or most significant influencing factor on another entity’s state, behavior, or outcome.
-
D.
sectorMostAffected
Indicates that a particular sector is the one experiencing the greatest impact or disruption relative to others in a given context.
-
E.
hasImpactArea
Indicates that an entity affects, influences, or has consequences within a specific area, domain, or scope.
- 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_69ef96b490ac8190a412d04c5d009f3e |
completed | April 27, 2026, 5:02 p.m. |
| NER | Named-entity recognition | batch_69ffa9677be08190852c8ef6c2545fed |
completed | May 9, 2026, 9:38 p.m. |
| PD | Predicate disambiguation | batch_69ffa6570e2c8190a9d7b37f12b91d9a |
completed | May 9, 2026, 9:25 p.m. |
Created at: April 27, 2026, 6:43 p.m.