Triple
T1508732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Subway Series |
E33962
|
entity |
| Predicate | rivalryLevel |
P29369
|
FINISHED |
| Object | high |
—
|
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: high | Statement: [Subway Series, rivalryLevel, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rivalryLevel Context triple: [Subway Series, rivalryLevel, high]
-
A.
typeOfRivalry
Indicates a competitive or adversarial relationship between entities, specifying the particular kind or nature of rivalry that exists between them.
-
B.
rivalryName
Indicates that a specific name or label is assigned to a rivalry relationship between two entities.
-
C.
hasRivalryEmotion
Indicates that one entity feels rivalry-based emotions, such as competitive tension or antagonistic comparison, toward another entity.
-
D.
rivalryCharacterization
Indicates a relationship in which two entities are characterized as rivals, typically defined by ongoing competition, opposition, or conflict between them.
-
E.
hasRivalryAspect
Indicates that there exists a competitive or adversarial relationship or dimension between entities.
- 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_69a885f352a4819099b24ff15489dede |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a8e2dd93dc8190a78443900e8d5564 |
completed | March 5, 2026, 1:56 a.m. |
| PD | Predicate disambiguation | batch_69a88728c150819095cdcdbfcabf4249 |
completed | March 4, 2026, 7:25 p.m. |
| PDg | Predicate description generation | batch_69a8e2dbc848819084eb0d50189fa967 |
completed | March 5, 2026, 1:56 a.m. |
Created at: March 4, 2026, 7:24 p.m.