Triple
T108697
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dallas Cowboys–Washington Commanders rivalry |
E2194
|
entity |
| Predicate | rivalryType |
P903
|
FINISHED |
| Object | divisional rivalry |
—
|
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: divisional rivalry | Statement: [Dallas Cowboys–Washington Commanders rivalry, rivalryType, divisional rivalry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rivalryType Context triple: [Dallas Cowboys–Washington Commanders rivalry, rivalryType, divisional rivalry]
-
A.
rivalryName
Indicates that a specific name or label is assigned to a rivalry relationship between two entities.
-
B.
divisionRivalry
chosen
Indicates a competitive or adversarial relationship between entities that belong to the same division or subgroup within a larger organization or system.
-
C.
notableRivalry
Indicates a significant, well-recognized competitive or adversarial relationship between two entities.
-
D.
typeOfCompetition
Indicates the specific kind or category of competition in which an entity participates or is involved.
-
E.
cityRivalry
Indicates a competitive or adversarial relationship that exists between two cities, often involving sports, economics, culture, or historical tensions.
- 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_69a24fcdaeb48190a2d796677e4b3281 |
completed | Feb. 28, 2026, 2:15 a.m. |
| NER | Named-entity recognition | batch_69a25711f6788190a22252ea3a3af394 |
completed | Feb. 28, 2026, 2:46 a.m. |
| PD | Predicate disambiguation | batch_69a2563fd2fc819090265edbfe3092d6 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:20 a.m.