Triple
T35826747
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sunyer |
E1035665
|
entity |
| Predicate | hasVehicleRegistrationCodeProvince |
P1173
|
FINISHED |
| Object | L |
—
|
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: L | Statement: [Sunyer, hasVehicleRegistrationCodeProvince, L]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVehicleRegistrationCodeProvince Context triple: [Sunyer, hasVehicleRegistrationCodeProvince, L]
-
A.
hasProvincialCode
Indicates that an entity is associated with a specific provincial code that identifies its province or administrative region.
-
B.
hasProvinceName
Indicates that an entity (such as a province or region) bears or is associated with a specific province name.
-
C.
hasProvinceType
Indicates that an entity is associated with a province classified by a specific type or category.
-
D.
hasProvinceOrRegion
Indicates that one entity includes, is associated with, or is located within a specific province or region as an administrative or geographic subdivision.
-
E.
vehicleRegistrationCode
chosen
Indicates the official registration identifier assigned to a vehicle, typically used for legal identification and record-keeping.
- 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_69f76e185ffc8190880b3cdf51decd38 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7aa699d68819081ed363931894ab3 |
completed | May 3, 2026, 8:04 p.m. |
| PD | Predicate disambiguation | batch_69f7a8d219f8819081dc4ce3c83ca0cb |
completed | May 3, 2026, 7:58 p.m. |
Created at: May 3, 2026, 4:06 p.m.