Triple
T11487318
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Florida International University Panthers |
E272311
|
entity |
| Predicate | womenTeamsCount |
P99758
|
FINISHED |
| Object | approximately 10 |
—
|
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 10 | Statement: [Florida International University Panthers, womenTeamsCount, approximately 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: womenTeamsCount Context triple: [Florida International University Panthers, womenTeamsCount, approximately 10]
-
A.
womenTeam
Indicates that the team is composed of women or is designated as a women’s team.
-
B.
hasGenderedTeams
Indicates that the entity organizes or participates in teams that are separated or defined based on gender.
-
C.
teamCountType
Indicates how the number of teams is categorized or measured within a given context.
-
D.
hasNumberOfTeams
Indicates the quantity of teams associated with or contained by a given entity.
-
E.
numberOfTeamsInUnitedStates
Indicates the total count of teams that are located within or belong to the United States.
- F. None of above. chosen
Provenance (4 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. |
| PDg | Predicate description generation | batch_69d822ef46988190a1c360da4ee14fef |
completed | April 9, 2026, 10:06 p.m. |
Created at: April 8, 2026, 9:36 p.m.