Triple
T35752085
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lotus F1 Team |
E1033344
|
entity |
| Predicate | constructorPoints2014 |
P165855
|
FINISHED |
| Object | 10 |
—
|
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 | Statement: [Lotus F1 Team, constructorPoints2014, 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: constructorPoints2014 Context triple: [Lotus F1 Team, constructorPoints2014, 10]
-
A.
constructorPointsContributionSeason
chosen
Indicates the number of championship points a constructor contributes or earns over the course of a specific season.
-
B.
afPoints
Indicates a relationship where a scoring or point value is assigned, tracked, or associated with an entity or interaction.
-
C.
constructorsChampionships
Indicates the number of championship titles won by a constructor (e.g., a racing team) in a given competition or series.
-
D.
KPoint
Indicates a relationship where a specific point is designated or identified as a key or reference point within a spatial or geometric context.
-
E.
pointsType
Indicates that one entity is classified as a specific type or category of points associated with another entity.
- 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_69f76e1262f48190a313318665acc189 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fd7fdafbe881908a31fcb407af2c34 |
completed | May 8, 2026, 6:16 a.m. |
| PD | Predicate disambiguation | batch_69fd7ef0ea908190b5d83f71565bdb1c |
completed | May 8, 2026, 6:13 a.m. |
Created at: May 3, 2026, 4:06 p.m.