Triple
T15972302
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Misty May-Treanor |
E387353
|
entity |
| Predicate | AVPTitleCount |
P4419
|
FINISHED |
| Object | over 100 career AVP tournament wins |
—
|
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: over 100 career AVP tournament wins | Statement: [Misty May-Treanor, AVPTitleCount, over 100 career AVP tournament wins]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: AVPTitleCount Context triple: [Misty May-Treanor, AVPTitleCount, over 100 career AVP tournament wins]
-
A.
titleCount
chosen
Indicates the number of distinct titles associated with an entity within a given context.
-
B.
totalFranchiseTitleNumber
Indicates the total count of titles associated with a given franchise across all its installments or entries.
-
C.
hostNationTitleCount
Indicates the number of titles won by the nation that is hosting a particular event or competition.
-
D.
numberOfTableGames
Indicates the quantity of table games associated with or available in relation to a given entity.
-
E.
ACCtitles
Indicates that one entity holds or is associated with academic or professional titles conferred by another entity.
- 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_69d86da94ccc819083d187f5dc6a123e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e173b3bf6c81909230170e833d7ce7 |
completed | April 16, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69e142d6fb588190b4176eab4bbae774 |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:54 a.m.