Triple
T2396041
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chevrolet Avalanche |
E47652
|
entity |
| Predicate | bedLength |
P37903
|
FINISHED |
| Object | approximately 5.3 ft with midgate up |
—
|
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: approximately 5.3 ft with midgate up | Statement: [Chevrolet Avalanche, bedLength, approximately 5.3 ft with midgate up]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bedLength Context triple: [Chevrolet Avalanche, bedLength, approximately 5.3 ft with midgate up]
-
A.
bedCount
Indicates the number of beds associated with an entity, such as a room, facility, or accommodation.
-
B.
beds
Indicates that one entity provides or designates a place for another entity to sleep or rest.
-
C.
cabinLengthM
Indicates the length of a cabin measured in meters.
-
D.
properLengthMeasuredIn
Indicates that the proper (intrinsic or rest-frame) length of an entity is expressed using a specified unit of measurement.
-
E.
numberOfBedrooms
Indicates the quantity of bedrooms associated with a given property or dwelling.
- 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_69a88a1c450c81909f61abb8b6863885 |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abc879b1b88190be8d0337d9a17bd0 |
completed | March 7, 2026, 6:40 a.m. |
| PD | Predicate disambiguation | batch_69abc5a3825c81909ec6111dfc165453 |
completed | March 7, 2026, 6:28 a.m. |
| PDg | Predicate description generation | batch_69abc664317c8190a6bb5a5065c21bde |
completed | March 7, 2026, 6:32 a.m. |
Created at: March 4, 2026, 7:57 p.m.