Triple
T15473346
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Big Ten wrestling |
E376718
|
entity |
| Predicate | hasRecruitingFootprint |
P10833
|
FINISHED |
| Object | national |
—
|
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: national | Statement: [Big Ten wrestling, hasRecruitingFootprint, national]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRecruitingFootprint Context triple: [Big Ten wrestling, hasRecruitingFootprint, national]
-
A.
recruitingFocus
Indicates that an entity’s recruitment efforts are specifically directed toward or concentrated on another entity or group.
-
B.
recruitingScope
chosen
Indicates the extent or boundaries within which recruiting activities are conducted or targeted.
-
C.
recruitmentBase
Indicates the foundational source, location, or context from which recruitment efforts or recruited entities originate.
-
D.
hasRecruitType
Indicates that an entity is associated with a specific type or category of recruitment.
-
E.
recruitmentFrom
Indicates that one entity recruits or sources members, employees, or participants from another entity.
- 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_69d85cd21dcc81908646251b1c26ea00 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f6c57308190b4cfe661c26addd4 |
completed | April 16, 2026, 1:46 a.m. |
| PD | Predicate disambiguation | batch_69ded284bd008190b31c53b4f1cebadd |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:33 a.m.