Triple
T35752569
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FIA Formula One 25–18–15–12–10–8–6–4–2–1 scoring system |
E1033354
|
entity |
| Predicate | winnerToSecondRatio |
P188831
|
FINISHED |
| Object | 25:18 |
—
|
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: 25:18 | Statement: [FIA Formula One 25–18–15–12–10–8–6–4–2–1 scoring system, winnerToSecondRatio, 25:18]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: winnerToSecondRatio Context triple: [FIA Formula One 25–18–15–12–10–8–6–4–2–1 scoring system, winnerToSecondRatio, 25:18]
-
A.
winnerPercentage
Indicates the proportion of wins an entity has achieved relative to the total number of attempts or competitions.
-
B.
secondRoundRunnerUpVoteSharePercentage
Indicates the percentage of total votes received by the candidate who finished as runner-up in the second round of a contest or election.
-
C.
rarityOfWinnersByPosition
Indicates how uncommon or infrequent winners are for each specific position.
-
D.
runnerUpWins
Indicates that an entity finishes in second place in a competition or ranking and receives the corresponding runner-up victory or award.
-
E.
winnerCount
Indicates the number of entities that are designated as winners in a given context or event.
- 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_69f76e1262f48190a313318665acc189 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fbad1e94988190b86d447a68e65067 |
completed | May 6, 2026, 9:05 p.m. |
| PD | Predicate disambiguation | batch_69fba881b8e0819094790935152b99a1 |
completed | May 6, 2026, 8:45 p.m. |
| PDg | Predicate description generation | batch_69fbad1b3ba08190ad69e21461333f2e |
completed | May 6, 2026, 9:05 p.m. |
Created at: May 3, 2026, 4:06 p.m.