Triple
T2568707
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Midwest League |
E57612
|
entity |
| Predicate | numberOfTeamsRange |
P18041
|
FINISHED |
| Object | approximately 12 to 16 teams over time |
—
|
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: approximately 12 to 16 teams over time | Statement: [Midwest League, numberOfTeamsRange, approximately 12 to 16 teams over time]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTeamsRange Context triple: [Midwest League, numberOfTeamsRange, approximately 12 to 16 teams over time]
-
A.
numberOfTeamsVariesBetween
chosen
Indicates that the count of teams involved changes within a specified range or across different instances or conditions.
-
B.
hasNumberOfTeams
Indicates the quantity of teams associated with or contained by a given entity.
-
C.
teamCountType
Indicates how the number of teams is categorized or measured within a given context.
-
D.
numberOfTeamsVariesByYear
Indicates that the number of teams involved changes depending on the specific year.
-
E.
numberOfTeamsInUnitedStates
Indicates the total count of teams that are located within or belong to the United States.
- 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_69ab4a51410081908501dcf8bad9adc4 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd36191848190b6255fa9029429bd |
completed | March 7, 2026, 7:27 a.m. |
| PD | Predicate disambiguation | batch_69abd0cc8d308190ae7aa32b8f5ae2e5 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:48 p.m.