Triple
T9873093
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sally Carrera |
E240004
|
entity |
| Predicate | licensePlateReference |
P90990
|
FINISHED |
| Object | Pixar employee company car plate |
—
|
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: Pixar employee company car plate | Statement: [Sally Carrera, licensePlateReference, Pixar employee company car plate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: licensePlateReference Context triple: [Sally Carrera, licensePlateReference, Pixar employee company car plate]
-
A.
licensePlateSystem
Indicates a system or scheme used to assign, format, and manage license plate identifiers for vehicles.
-
B.
licensePlateOrigin
Indicates that a vehicle’s license plate was issued or originates from a particular jurisdiction or region.
-
C.
hasPlate
Indicates that one entity possesses, is equipped with, or includes a plate as part of its attributes or components.
-
D.
registrationNumber
Indicates the unique identifier assigned to an entity as part of an official or formal registration process.
-
E.
registrationPlateFormat
Indicates the specific pattern or structure that a vehicle’s registration plate must follow (such as the arrangement of letters, numbers, and symbols).
- 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_69ca84e8a0788190b9061811d50fd554 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3f754008190abe3fe034b42908e |
completed | April 2, 2026, 12:10 a.m. |
| PD | Predicate disambiguation | batch_69cd1d7621d48190aa6a6f34399514b0 |
completed | April 1, 2026, 1:28 p.m. |
| PDg | Predicate description generation | batch_69cd3581a9688190a00cef4c3eebb0ae |
completed | April 1, 2026, 3:10 p.m. |
Created at: March 30, 2026, 8:37 p.m.