Triple
T26924740
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Intel Grand Slam |
E677746
|
entity |
| Predicate | season2EventLimit |
P2438
|
FINISHED |
| Object | 10 consecutive eligible events |
—
|
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: 10 consecutive eligible events | Statement: [Intel Grand Slam, season2EventLimit, 10 consecutive eligible events]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: season2EventLimit Context triple: [Intel Grand Slam, season2EventLimit, 10 consecutive eligible events]
-
A.
numberOfEvents
chosen
Indicates the quantity or count of events associated with a given entity or context.
-
B.
seasonNumber
Indicates the ordinal position of a season within a series or sequence of seasons.
-
C.
seasonCountDetail
Indicates the specific number of seasons associated with something, often including additional contextual details about that season count.
-
D.
partOfSeasonCount
Indicates that an entity represents the number of parts or segments that make up a particular season.
-
E.
numberOfMainEvents
Indicates the total count of primary or most significant events associated with a given entity or context.
- 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_69eee9bdebc48190ba90a12a63e09c73 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69f6afebd7ec8190ab696f363d84abf0 |
completed | May 3, 2026, 2:16 a.m. |
| PD | Predicate disambiguation | batch_69f6aca204148190850a3dc325bc07b7 |
completed | May 3, 2026, 2:02 a.m. |
Created at: April 27, 2026, 6:09 a.m.