Triple
T15843320
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tiago Monteiro |
E384150
|
entity |
| Predicate | F1BestFinish |
P82783
|
FINISHED |
| Object | 3rd place |
—
|
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: 3rd place | Statement: [Tiago Monteiro, F1BestFinish, 3rd place]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: F1BestFinish Context triple: [Tiago Monteiro, F1BestFinish, 3rd place]
-
A.
bestRaceFinishPositionAchievedByDriver
chosen
Indicates the best (highest-ranking) race finishing position that a particular driver has ever achieved.
-
B.
bestFormulaOneChampionshipPosition
Indicates the highest (best) finishing position an entity has ever achieved in a Formula One World Championship season.
-
C.
F1LapRecordHolder
Indicates that the subject holds the fastest lap record in a Formula 1 race or at a specific Formula 1 circuit.
-
D.
winnerLaps
Indicates that one participant completed more laps than another, thereby winning based on lap count.
-
E.
F1Points
Indicates that an entity has earned or is associated with a specific number of Formula 1 championship points.
- 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_69d86da422088190aac39e32e6c68429 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e142ea3da08190a9d2d5917f84907c |
completed | April 16, 2026, 8:13 p.m. |
| PD | Predicate disambiguation | batch_69e005434ed88190baf11c169da3cf29 |
completed | April 15, 2026, 9:38 p.m. |
Created at: April 10, 2026, 4:50 a.m.