Triple
T3738749
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Midland F1 Racing |
E79647
|
entity |
| Predicate | scoredConstructorsPoints |
P51165
|
FINISHED |
| Object | false |
—
|
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: false | Statement: [Midland F1 Racing, scoredConstructorsPoints, false]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: scoredConstructorsPoints Context triple: [Midland F1 Racing, scoredConstructorsPoints, false]
-
A.
scoringLeaderPoints
Indicates the number of points scored by the leading scorer in a game, season, or competition.
-
B.
scoring
Indicates the act of achieving points or a measurable result, typically by successfully completing an action that contributes to a score or outcome.
-
C.
rankingPoints
Indicates the number of points assigned to an entity based on its position or performance in a ranking or competition.
-
D.
scoringType
Indicates the method or criteria by which performance, outcomes, or results are evaluated and assigned a score in a given context.
-
E.
scoredFor
Indicates that one entity achieved points or a score on behalf of another entity, such as a player scoring for a team.
- 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_69ad8b115610819095b02007da5ca3cb |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcb404b908190b6b4ee583dee3cc9 |
completed | March 8, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_69adc048f28c819092bed16a95a3cac1 |
completed | March 8, 2026, 6:30 p.m. |
| PDg | Predicate description generation | batch_69adc198b95481908ca6e4e875aae446 |
completed | March 8, 2026, 6:36 p.m. |
Created at: March 8, 2026, 3:34 p.m.