Triple
T13290870
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2015 Formula One season |
E316555
|
entity |
| Predicate | doublePointsFinalRace |
P109359
|
FINISHED |
| Object | no |
—
|
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: no | Statement: [2015 Formula One season, doublePointsFinalRace, no]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: doublePointsFinalRace Context triple: [2015 Formula One season, doublePointsFinalRace, no]
-
A.
runnersUpPoints
Indicates the number of points awarded to an entity for finishing as a runner-up in a competition or ranking.
-
B.
finalRanking2
Indicates the final ordered position or placement of one entity relative to others in a ranking or competition.
-
C.
finalScore
Indicates the resulting or overall score achieved after all contributing actions, events, or evaluations are completed.
-
D.
finalSecondLegScore
Indicates the score achieved in the second leg of a two-leg competition or matchup once that leg is completed.
-
E.
totalPointsByChampionInFinal
Indicates the total number of points scored by a given champion in the final match or round.
- 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_69d806b349908190a9a61dd9323bf153 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99cfdc9388190af1fdd3cd4717bd8 |
completed | April 11, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69d98f6893708190aeebf4c47386cff7 |
completed | April 11, 2026, 12:01 a.m. |
| PDg | Predicate description generation | batch_69d99cf7f9c48190a6a4f452b4a2aefa |
completed | April 11, 2026, 12:59 a.m. |
Created at: April 9, 2026, 9:27 p.m.