Triple
T9339841
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dallas Mavericks–New York Knicks games |
E224735
|
entity |
| Predicate | typicalSeasonSeriesLength |
P87601
|
FINISHED |
| Object | 2 games |
—
|
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 games | Statement: [Dallas Mavericks–New York Knicks games, typicalSeasonSeriesLength, 2 games]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSeasonSeriesLength Context triple: [Dallas Mavericks–New York Knicks games, typicalSeasonSeriesLength, 2 games]
-
A.
typicalSeasonCovered
Indicates the season or time of year that is most commonly encompassed or represented by something.
-
B.
numberOfSeasons
Indicates the total count of seasons associated with a particular entity (such as a series, competition, or event).
-
C.
typicalEndSeason
Indicates the season in which something (such as an activity, event, or condition) usually or characteristically comes to an end.
-
D.
typicalStartSeason
Indicates the season during which something (such as an activity, event, or phenomenon) usually begins.
-
E.
typicalNumberOfStopsPerSeason
Indicates the usual or average count of stops that occur in a single season.
- 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_69ca84286fcc81909f6e7fd7a7e862a2 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd4bae2e2481909effc2dc89a642c5 |
completed | April 1, 2026, 4:45 p.m. |
| PD | Predicate disambiguation | batch_69cc7a66aef08190b8d668cff5b04f5f |
completed | April 1, 2026, 1:52 a.m. |
| PDg | Predicate description generation | batch_69cc94b796788190816b71b1e9996288 |
completed | April 1, 2026, 3:44 a.m. |
Created at: March 30, 2026, 7:40 p.m.