Triple
T845620
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Region |
E18270
|
entity |
| Predicate | numberOfTeamsVariesByYear |
P20581
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [South Region, numberOfTeamsVariesByYear, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTeamsVariesByYear Context triple: [South Region, numberOfTeamsVariesByYear, true]
-
A.
numberOfTeamsVariesBetween
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.
numberOfTeamsInUnitedStates
Indicates the total count of teams that are located within or belong to the United States.
-
D.
formerNumberOfTeams
Indicates the number of teams that an entity previously had before a change or reorganization.
-
E.
typicalNumberOfCWSTeams
Indicates the usual or standard number of CWS teams associated with or involved in a given context or entity.
- 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_69a4938b04208190b82e1df6b572c548 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac0a91e48190b4349ae8bb67fd90 |
completed | March 1, 2026, 9:13 p.m. |
| PD | Predicate disambiguation | batch_69a4aa7f396c8190aa8b8dfbe0b4f732 |
completed | March 1, 2026, 9:07 p.m. |
| PDg | Predicate description generation | batch_69a4ab4893e481908632102d240466dc |
completed | March 1, 2026, 9:10 p.m. |
Created at: March 1, 2026, 7:38 p.m.