Triple
T11487317
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Florida International University Panthers |
E272311
|
entity |
| Predicate | menTeamsCount |
P6986
|
FINISHED |
| Object | approximately 7 |
—
|
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: approximately 7 | Statement: [Florida International University Panthers, menTeamsCount, approximately 7]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: menTeamsCount Context triple: [Florida International University Panthers, menTeamsCount, approximately 7]
-
A.
teamCountType
Indicates how the number of teams is categorized or measured within a given context.
-
B.
mostTeamsInCountry
Indicates that an entity has the highest number of teams located within a given country compared to all other entities.
-
C.
hasNumberOfTeams
chosen
Indicates the quantity of teams associated with or contained by a given entity.
-
D.
hasMenTeam
Indicates that an entity possesses, is associated with, or fields a men’s team.
-
E.
gameCountPerTeam
Indicates the number of games associated with or played by each team.
- 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_69d6aae1b09881909ce2ded3fa0c14fa |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d85a1fc9688190aacc2eed64229b79 |
completed | April 10, 2026, 2:02 a.m. |
| PD | Predicate disambiguation | batch_69d808736c5c8190899b5b3b2e797f65 |
completed | April 9, 2026, 8:13 p.m. |
Created at: April 8, 2026, 9:36 p.m.