Triple
T15561726
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NCAA women’s water polo |
E371015
|
entity |
| Predicate | teamSizeInPlay |
P89523
|
FINISHED |
| Object | 7 players in the water per team |
—
|
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: 7 players in the water per team | Statement: [NCAA women’s water polo, teamSizeInPlay, 7 players in the water per team]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: teamSizeInPlay Context triple: [NCAA women’s water polo, teamSizeInPlay, 7 players in the water per team]
-
A.
rosterSize
Indicates the total number of individuals included on a given roster.
-
B.
typicalTeamSize
Indicates the usual or most common number of members that make up a given team.
-
C.
numberOfPlayersPerTeam
chosen
Indicates the quantity of players that are assigned to or allowed on each team in a given context.
-
D.
playerNumber
Indicates the specific jersey or identification number assigned to a player within a team or game context.
-
E.
teamCountType
Indicates how the number of teams is categorized or measured within a given context.
- 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_69d85cc6cf40819091f4a5facee1ebe6 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04ddb4c0c81909b3f4c75c91f7f3f |
completed | April 16, 2026, 2:47 a.m. |
| PD | Predicate disambiguation | batch_69deda7e6e748190b29ccce23298afef |
completed | April 15, 2026, 12:23 a.m. |
Created at: April 10, 2026, 4:09 a.m.