Triple
T37133331
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Warroad |
E919584
|
entity |
| Predicate | hasNotableHighSchoolSport |
P205743
|
FINISHED |
| Object | high school ice hockey |
—
|
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: high school ice hockey | Statement: [Warroad, hasNotableHighSchoolSport, high school ice hockey]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableHighSchoolSport Context triple: [Warroad, hasNotableHighSchoolSport, high school ice hockey]
-
A.
hasNotableHighSchool
Indicates that an entity is associated with a high school that is particularly notable or significant in some recognized way.
-
B.
playedCollegeSport
Indicates that the subject participated in an organized college-level sport for the object institution.
-
C.
hasNotableSportAlumnus
Indicates that an institution or organization has at least one alumnus who is notable for achievements in sports.
-
D.
playedHighSchoolBasketballAt
Indicates that a person was a member of and participated on the basketball team of a particular high school.
-
E.
playedHighSchoolBasketballIn
Indicates that a person participated in playing basketball at the high school level in a specified location or institution.
- 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_69f76e9d13e48190a108f7fbf80ff375 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a037cad051c8190b28b354b89208574 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a11efc08190bb7cacc1325b4dc6 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c842b2c819082f1d2db995ac2eb |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:15 p.m.