Triple
T27486531
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gruyère cheese |
E693752
|
entity |
| Predicate | typicalWheelDiameter |
P44248
|
FINISHED |
| Object | 55 to 65 centimeters |
—
|
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: 55 to 65 centimeters | Statement: [Gruyère cheese, typicalWheelDiameter, 55 to 65 centimeters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalWheelDiameter Context triple: [Gruyère cheese, typicalWheelDiameter, 55 to 65 centimeters]
-
A.
wheelDiameter
chosen
Indicates the size of a wheel measured across its diameter.
-
B.
formerWheelDiameter
Indicates that one entity was previously the wheel diameter of another entity, but is no longer its current wheel diameter.
-
C.
driverDiameter
Indicates the size of the circular cross-section of a driver component, typically measured as the distance across its widest point.
-
D.
typicalCaseDiameter
Indicates the usual or standard diameter value associated with an object or case in typical conditions.
-
E.
hasSmallDiameterWheels
Indicates that the subject is equipped with wheels whose diameter is relatively small compared to a standard or reference size.
- 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_69ef5382b9648190be0b1ef2ad5d043c |
completed | April 27, 2026, 12:16 p.m. |
| NER | Named-entity recognition | batch_69ffb5c373948190a6606e8caa87a384 |
completed | May 9, 2026, 10:31 p.m. |
| PD | Predicate disambiguation | batch_69ffb261da788190b41399df8ed895e8 |
completed | May 9, 2026, 10:17 p.m. |
Created at: April 27, 2026, 1:02 p.m.