Triple
T4438395
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ivy League men’s cross country |
E95708
|
entity |
| Predicate | numberOfMemberTeams |
P6986
|
FINISHED |
| Object | 8 |
—
|
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: 8 | Statement: [Ivy League men’s cross country, numberOfMemberTeams, 8]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMemberTeams Context triple: [Ivy League men’s cross country, numberOfMemberTeams, 8]
-
A.
hasNumberOfTeams
chosen
Indicates the quantity of teams associated with or contained by a given entity.
-
B.
teamCountType
Indicates how the number of teams is categorized or measured within a given context.
-
C.
numberOfTeamsInUnitedStates
Indicates the total count of teams that are located within or belong to the United States.
-
D.
numberOfTeamsVariesBetween
Indicates that the count of teams involved changes within a specified range or across different instances or conditions.
-
E.
numberOfMemberOrganizations
Indicates the total count of organizations that are members of a given group, association, or umbrella 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_69b3453ea2b48190a26f154b3b8fece5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3558c75948190875a5fb10eac0597 |
completed | March 13, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69b34f6078cc8190831b89f404198cc5 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:31 p.m.