Triple
T35564144
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2015 Daytona 500 |
E1027720
|
entity |
| Predicate | distanceUnits |
P145577
|
FINISHED |
| Object | miles |
—
|
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: miles | Statement: [2015 Daytona 500, distanceUnits, miles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceUnits Context triple: [2015 Daytona 500, distanceUnits, miles]
-
A.
hasDistanceUnit
chosen
Indicates that a specified unit of measurement is used to express a distance value in the relationship.
-
B.
approximateDistanceUnit
Indicates that the distance value is expressed in an approximate or non-exact unit of measurement.
-
C.
distanceMeasurementMethod
Indicates the method or technique used to determine or measure the distance between entities.
-
D.
positionUnit
Indicates that a position or role is associated with, or belongs to, a specific organizational unit.
-
E.
dimensions_km
Indicates the physical size or extent of something measured in kilometers, typically specifying one or more linear dimensions (e.g., length, width, height).
- 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_69f76e020fd8819081cb080e7e203083 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037c8d06cc8190ab6a5e18d9d2571e |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a04d8348190a4819666eab42c9b |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:04 p.m.