Triple
T17174642
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | women's sabre |
E416826
|
entity |
| Predicate | boutFormatCollegeDual |
P126610
|
FINISHED |
| Object | 5-touch bouts |
—
|
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: 5-touch bouts | Statement: [women's sabre, boutFormatCollegeDual, 5-touch bouts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: boutFormatCollegeDual Context triple: [women's sabre, boutFormatCollegeDual, 5-touch bouts]
-
A.
namedForCollege
Indicates that an entity is named after or in honor of a particular college.
-
B.
formerCollege
Indicates that one entity previously attended or was enrolled at the other entity as a college, but no longer is.
-
C.
playedForCollegeTeamFrom
Indicates that an individual was a member of and played for a specific college team starting in a given year.
-
D.
hasColleges
Indicates that an entity possesses, contains, or is associated with one or more colleges.
-
E.
hasAffiliatedCollegesIn
Indicates that an institution maintains affiliated colleges located within a specified geographic area or jurisdiction.
- 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_69d886d5f34c8190b24564dfaa63f3fb |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3fc0c329081909f118bd4b7be8653 |
completed | April 18, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69e383141ae0819096acd71683637cbc |
completed | April 18, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69e39c2fedb881908bfed2c3e5f2616a |
completed | April 18, 2026, 2:58 p.m. |
Created at: April 10, 2026, 5:37 a.m.