Triple
T25350660
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Buck Hill |
E635672
|
entity |
| Predicate | hasNotableAthleteTrained |
P178745
|
FINISHED |
| Object | Lindsey Vonn |
—
|
NE NERFINISHED |
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: Lindsey Vonn | Statement: [Buck Hill, hasNotableAthleteTrained, Lindsey Vonn]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableAthleteTrained Context triple: [Buck Hill, hasNotableAthleteTrained, Lindsey Vonn]
-
A.
hasAthlete
Indicates a relationship where an entity (such as a team, organization, or event) includes or is associated with one or more athletes.
-
B.
hasNotablePlayer
Indicates that an entity (such as a team or club) is associated with a player who is particularly distinguished, famous, or significant in its context.
-
C.
hasNotableSportAlumnus
Indicates that an institution or organization has at least one alumnus who is notable for achievements in sports.
-
D.
notableAthlete
Indicates that the subject is a well-known or distinguished athlete associated with the object (such as a sport, team, or organization).
-
E.
coachedAthletesAt
Indicates a relationship where a coach has trained or instructed athletes at a particular organization, team, or location.
- 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_69e75a9ac5d881909387ed766e20cd47 |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f71422adac8190a5ceb32dcf820833 |
completed | May 3, 2026, 9:23 a.m. |
| PD | Predicate disambiguation | batch_69f712764d2c819081b64b27e5de4a13 |
completed | May 3, 2026, 9:16 a.m. |
| PDg | Predicate description generation | batch_69f71421e8d08190807ccfb15d0f0ddb |
completed | May 3, 2026, 9:23 a.m. |
Created at: April 21, 2026, 1:34 p.m.