Triple
T23020337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Texas A&M University–Commerce Lions |
E573145
|
entity |
| Predicate | footballNationalTitlesDivisionII |
P150705
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [Texas A&M University–Commerce Lions, footballNationalTitlesDivisionII, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: footballNationalTitlesDivisionII Context triple: [Texas A&M University–Commerce Lions, footballNationalTitlesDivisionII, 1]
-
A.
numberOfSegundaDivisionTitles
Indicates the number of Segunda Division championship titles that an entity has won.
-
B.
secondDivisionNumberOfClubs
Indicates the total number of clubs that participate in the second division of a competition or league.
-
C.
divisionTitlesWonWith
Indicates that one entity has won a specified number of division titles in association with another entity (such as a team, league, or organization).
-
D.
secondDivisionName
Indicates the name assigned to a second-level division or subdivision within a larger organizational or administrative structure.
-
E.
team2DivisionTitles
Indicates the number of division championship titles that the second team has won.
- 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_69e245b821008190b0e09cb02092aae1 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f183e8324c81908b8868d298af66e1 |
completed | April 29, 2026, 4:07 a.m. |
| PD | Predicate disambiguation | batch_69ef3ba004a48190885aece88efd1f52 |
completed | April 27, 2026, 10:34 a.m. |
| PDg | Predicate description generation | batch_69ef538b29c081908fa56ee35a1dcee7 |
completed | April 27, 2026, 12:16 p.m. |
Created at: April 17, 2026, 3:52 p.m.