Triple
T2633909
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brazilian Grand Prix |
E59697
|
entity |
| Predicate | trackLengthCategory |
P3832
|
FINISHED |
| Object | one of the shorter F1 circuits |
—
|
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: one of the shorter F1 circuits | Statement: [Brazilian Grand Prix, trackLengthCategory, one of the shorter F1 circuits]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trackLengthCategory Context triple: [Brazilian Grand Prix, trackLengthCategory, one of the shorter F1 circuits]
-
A.
trailLengthApprox
Indicates an approximate measurement of the total length of a trail.
-
B.
typicalLength
Indicates the usual or characteristic length associated with an entity or phenomenon.
-
C.
trackType
chosen
Indicates the specific kind or category of track associated with an entity, such as its functional or physical classification.
-
D.
hasRunwayLengthCategory
Indicates that an airport or airfield is associated with a specific categorical range of runway lengths (e.g., short, medium, long).
-
E.
hasNameLengthCategory
Indicates that an entity is associated with a classification describing the length of its name (e.g., short, medium, long).
- 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_69ab4ac8596c8190b34997e73d9e991c |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abdb0e7b888190bfa5d2e33f00ec0f |
completed | March 7, 2026, 8 a.m. |
| PD | Predicate disambiguation | batch_69abd810d7f481908e81c305772c4c14 |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:50 p.m.