Triple
T22393963
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Midge Purce |
E553580
|
entity |
| Predicate | hasNumberOfPlayersOnTeamType |
P89523
|
FINISHED |
| Object | 11-a-side soccer |
—
|
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: 11-a-side soccer | Statement: [Midge Purce, hasNumberOfPlayersOnTeamType, 11-a-side soccer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfPlayersOnTeamType Context triple: [Midge Purce, hasNumberOfPlayersOnTeamType, 11-a-side soccer]
-
A.
sportNumberOfPlayersOnTeam
Indicates the number of players that make up a single team in a given sport.
-
B.
usesNumberOfPlayersOnFieldPerTeam
Indicates that the relationship specifies or depends on how many players each team has on the field at a given time.
-
C.
numberOfPlayersPerTeam
chosen
Indicates the quantity of players that are assigned to or allowed on each team in a given context.
-
D.
hasNumberOfTeams
Indicates the quantity of teams associated with or contained by a given entity.
-
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_69e11e4cf87c8190a1ff474daec326b7 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1585ce39c819082b62e7f2e297d9b |
completed | April 29, 2026, 1:01 a.m. |
| PD | Predicate disambiguation | batch_69e73015484c8190a9a0b9f554b61a81 |
completed | April 21, 2026, 8:06 a.m. |
Created at: April 16, 2026, 8:45 p.m.