Triple
T69087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Hockey League |
E1381
|
entity |
| Predicate | numberOfTeamsInUnitedStates |
P3760
|
FINISHED |
| Object | 25 |
—
|
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: 25 | Statement: [National Hockey League, numberOfTeamsInUnitedStates, 25]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTeamsInUnitedStates Context triple: [National Hockey League, numberOfTeamsInUnitedStates, 25]
-
A.
numberOfAmericanLeagueTeams
Indicates the total count of teams that belong to the American League.
-
B.
numberOfStates
Indicates the total count of distinct states or conditions associated with an entity or system.
-
C.
numberOfMemberStates
Indicates the total count of member states associated with a given entity or organization.
-
D.
numberOfNationalLeagueTeams
Indicates the quantity of teams that participate in the National League associated with a given entity.
-
E.
collegeTeam
Indicates that one entity is a sports team that represents or is affiliated with a particular college or university.
- 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_69a24c06b3bc8190aa4ac89026115efc |
completed | Feb. 28, 2026, 1:59 a.m. |
| NER | Named-entity recognition | batch_69a24fd16c248190a6ee4cd96c388772 |
completed | Feb. 28, 2026, 2:15 a.m. |
| PD | Predicate disambiguation | batch_69a24ea8cfd081908a26edad2473dde3 |
completed | Feb. 28, 2026, 2:10 a.m. |
| PDg | Predicate description generation | batch_69a24fcf5a88819088c5fa4c08476358 |
completed | Feb. 28, 2026, 2:15 a.m. |
Created at: Feb. 28, 2026, 2:03 a.m.