Triple
T294752
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dallas Cowboys–Philadelphia Eagles rivalry |
E6068
|
entity |
| Predicate | divisionGamesPerSeasonBetweenTeams |
P10399
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Dallas Cowboys–Philadelphia Eagles rivalry, divisionGamesPerSeasonBetweenTeams, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: divisionGamesPerSeasonBetweenTeams Context triple: [Dallas Cowboys–Philadelphia Eagles rivalry, divisionGamesPerSeasonBetweenTeams, 2]
-
A.
teamsPerDivision
Indicates the number of teams that are associated with or belong to each division.
-
B.
regularSeasonGamesPerTeam
Indicates the number of games each team plays during the regular season.
-
C.
totalMatchesPerSeason
Indicates the total number of matches associated with an entity within a single season.
-
D.
hasNumberOfTeams
Indicates the quantity of teams associated with or contained by a given entity.
-
E.
teamDivision
Indicates how a larger team is split into smaller subgroups or units for organization or collaboration.
- 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_69a2e79114b081909490b3bf5a5dbb51 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2e9e273f88190ac5355d1310376ed |
completed | Feb. 28, 2026, 1:13 p.m. |
| PD | Predicate disambiguation | batch_69a2e9368894819093eeae4347dfcc5a |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2e9e0d85c8190ae52662d83ea67fe |
completed | Feb. 28, 2026, 1:13 p.m. |
Created at: Feb. 28, 2026, 1:06 p.m.