Triple
T32989597
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Syracuse Orange men’s cross country |
E844046
|
entity |
| Predicate | NCAAteamChampionshipsCount |
P24536
|
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: [Syracuse Orange men’s cross country, NCAAteamChampionshipsCount, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: NCAAteamChampionshipsCount Context triple: [Syracuse Orange men’s cross country, NCAAteamChampionshipsCount, 1]
-
A.
NCAAChampionshipCount
chosen
Indicates the number of NCAA championships an entity has won.
-
B.
collegeTeamChampionships
Indicates the championships or titles that a college sports team has won.
-
C.
numberOfNationalChampionships
Indicates the total count of national championship titles an entity has won.
-
D.
nationalChampionshipParticipation
Indicates that an entity has taken part in a national-level championship competition.
-
E.
consecutiveNCAAChampionships
Indicates that the subject has won NCAA championships in consecutive years, with the object specifying the number of such back-to-back titles.
- 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_69f3494d99988190b502c68926af2c4d |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6d74b20a48190900dda1014cc13a8 |
completed | May 3, 2026, 5:04 a.m. |
| PD | Predicate disambiguation | batch_69f6d26f27dc8190ae426a3e1573933e |
completed | May 3, 2026, 4:43 a.m. |
Created at: May 1, 2026, 1:22 a.m.