Triple
T35752566
| 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 | pointsForTenthPlace |
P186500
|
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: [FIA Formula One 25–18–15–12–10–8–6–4–2–1 scoring system, pointsForTenthPlace, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pointsForTenthPlace Context triple: [FIA Formula One 25–18–15–12–10–8–6–4–2–1 scoring system, pointsForTenthPlace, 1]
-
A.
pointsForNinthPlace
Indicates the number of points awarded to an entity for finishing in ninth place in a ranking or competition.
-
B.
pointsForSeventhPlace
Indicates the number of points awarded to an entity for finishing in seventh place in a ranking or competition.
-
C.
pointsForEighthPlace
Indicates the number of points awarded to an entity that finishes in eighth place in a ranking or competition.
-
D.
pointsForSixthPlace
Indicates the number of points awarded to an entity for finishing in sixth place in a ranking or competition.
-
E.
pointsForThirdPlace
Indicates the number of points awarded to an entity for finishing in third place in a ranking or competition.
- 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_69f9fe1a1ca4819084c196f0041f0be2 |
completed | May 5, 2026, 2:26 p.m. |
| PD | Predicate disambiguation | batch_69f7cf769338819092a5f42653dcc956 |
completed | May 3, 2026, 10:43 p.m. |
| PDg | Predicate description generation | batch_69f9fd66eed48190bdc26a8def328c2d |
completed | May 5, 2026, 2:23 p.m. |
Created at: May 3, 2026, 4:06 p.m.