Triple
T3738754
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Midland F1 Racing |
E79647
|
entity |
| Predicate | primaryCarNumberUsed |
P51166
|
FINISHED |
| Object | 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: 18 | Statement: [Midland F1 Racing, primaryCarNumberUsed, 18]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryCarNumberUsed Context triple: [Midland F1 Racing, primaryCarNumberUsed, 18]
-
A.
vehicleUsed
Indicates that a particular vehicle is utilized or employed in performing an action, event, or activity.
-
B.
primaryTransportModel
Indicates that one transport model is designated as the main or default model used for a given context or entity.
-
C.
hasPrimaryVehicularAccessTo
Indicates that one location or entity serves as the main route or means by which vehicles can reach or enter another location or entity.
-
D.
intendedVehicle
Indicates that one entity is the vehicle that another entity plans or is meant to use.
-
E.
registrationNumber
Indicates the unique identifier assigned to an entity as part of an official or formal registration process.
- 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.