Triple
T36462333
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KG |
E898319
|
entity |
| Predicate | hasPlateFormatExample |
P64323
|
FINISHED |
| Object | KG AB 1234 |
—
|
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: KG AB 1234 | Statement: [KG, hasPlateFormatExample, KG AB 1234]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPlateFormatExample Context triple: [KG, hasPlateFormatExample, KG AB 1234]
-
A.
hasPlate
Indicates that one entity possesses, is equipped with, or includes a plate as part of its attributes or components.
-
B.
hasExample
Indicates that one entity serves as an instance, illustration, or concrete example of another entity.
-
C.
registrationPlateFormat
chosen
Indicates the specific pattern or structure that a vehicle’s registration plate must follow (such as the arrangement of letters, numbers, and symbols).
-
D.
usesCodeOnPlates
Indicates that an entity applies or employs a specific code or coding system on plates.
-
E.
hasExhibitFormat
Indicates the specific format or medium in which an exhibit is presented or made available.
- 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_69f76e58ebd88190b75d9b169b59d793 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fddd373cdc8190be1b12e70e4deb1f |
completed | May 8, 2026, 12:55 p.m. |
| PD | Predicate disambiguation | batch_69fddc6915a88190ad41e379aa3ede13 |
completed | May 8, 2026, 12:51 p.m. |
Created at: May 3, 2026, 4:10 p.m.