Triple
T27750324
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Backgammon |
E702097
|
entity |
| Predicate | hasStandardCheckerCountPerPlayer |
P190655
|
FINISHED |
| Object | 15 |
—
|
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: 15 | Statement: [Backgammon, hasStandardCheckerCountPerPlayer, 15]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStandardCheckerCountPerPlayer Context triple: [Backgammon, hasStandardCheckerCountPerPlayer, 15]
-
A.
hasLeadCountPerSide
Indicates the number of lead elements or units associated with each side in a given context or configuration.
-
B.
hasPlayerCount
Indicates the number of players associated with or participating in a given entity or activity.
-
C.
hasCheckInCounters
Indicates that an entity is associated with one or more check-in counters used for processing arrivals or registrations.
-
D.
hasNumberOfHands
Indicates the specific count of hands that an entity possesses.
-
E.
usesNumberOfPlayersOnFieldPerTeam
Indicates that the relationship specifies or depends on how many players each team has on the field at a given time.
- 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_69ef6a53c7388190899baa6daf42301c |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69fcce2cf9188190b3f65b362203a6a3 |
completed | May 7, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69fcccee6240819084680887731ff64b |
completed | May 7, 2026, 5:33 p.m. |
| PDg | Predicate description generation | batch_69fccdd2d84481909a7ce22407def9c7 |
completed | May 7, 2026, 5:37 p.m. |
Created at: April 27, 2026, 4:19 p.m.