Triple
T13290813
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2014 Formula One season |
E316554
|
entity |
| Predicate | constructorsChampionTitleNumberForMercedes |
P109352
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [2014 Formula One season, constructorsChampionTitleNumberForMercedes, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: constructorsChampionTitleNumberForMercedes Context triple: [2014 Formula One season, constructorsChampionTitleNumberForMercedes, 1]
-
A.
championshipWinningCar
Indicates that a car is the specific vehicle that won a particular championship.
-
B.
championCityTitleNumber
Indicates the ordinal number of championship titles that a particular city has won or holds in a given competition or league.
-
C.
consecutiveTitleNumberForChampion
Indicates that the associated number represents how many titles a champion has won consecutively up to that point.
-
D.
carNumberInF1
Indicates the specific racing number assigned to a driver or car in Formula 1 competition.
-
E.
GrandPrixTitle
Indicates that an entity has won a championship or overall title in a Grand Prix competition or series.
- 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.