Triple
T35752265
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baku City Circuit |
E1033348
|
entity |
| Predicate | firstFormulaOneRaceName |
P206033
|
FINISHED |
| Object | European Grand Prix |
E1791960
|
NE 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: European Grand Prix | Statement: [Baku City Circuit, firstFormulaOneRaceName, European Grand Prix]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstFormulaOneRaceName Context triple: [Baku City Circuit, firstFormulaOneRaceName, European Grand Prix]
-
A.
firstFormulaOneGrandPrix
Indicates the event at which an entity made its debut participation in a Formula One Grand Prix.
-
B.
enteredFormulaOne
Indicates that an entity began competing in Formula One racing, marking its entry into the Formula One championship.
-
C.
firstF1RaceHeld
Indicates that the subject is the location or venue where the first Formula 1 race was held.
-
D.
firstFormulaOneWin
Indicates that the subject achieved their first victory in a Formula One race in relation to the specified event or context.
-
E.
firstFormulaOneTeam
Indicates the Formula One team for which an entity (typically a driver) first competed.
- F. None of above. chosen
Provenance (5 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_6a037ce70f54819082946dad8d380825 |
completed | May 12, 2026, 7:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3891585a2081908d594dc0e7a57fde |
completed | June 22, 2026, 1:35 a.m. |
| PD | Predicate disambiguation | batch_6a037a069e6c8190857b611fffb7b867 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037ce53de881908cf14141cf3bc570 |
completed | May 12, 2026, 7:17 p.m. |
Created at: May 3, 2026, 4:06 p.m.