Triple
T15856805
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ludwigsburg |
E384479
|
entity |
| Predicate | vehicleRegistrationCode |
P1173
|
FINISHED |
| Object | LB |
E483344
|
NE 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: LB | Statement: [Ludwigsburg, vehicleRegistrationCode, LB]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LB Context triple: [Ludwigsburg, vehicleRegistrationCode, LB]
-
A.
LB
LB is the two-letter ISO 3166-1 alpha-2 country code representing Lebanon.
-
B.
LB
LB is the former New York Stock Exchange ticker symbol for L Brands, the American retail company that owned brands such as Victoria’s Secret and Bath & Body Works.
-
C.
LB
chosen
LB is the vehicle registration code for the Ludwigsburg district in the Stuttgart administrative region of Baden-Württemberg, Germany.
-
D.
BL
BL is the vehicle registration code used for the city of Banja Luka in Bosnia and Herzegovina.
-
E.
BL
BL is the commonly used abbreviation for British Leyland, a major former UK vehicle manufacturer known for producing a wide range of cars, trucks, and buses in the 20th century.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d86da422088190aac39e32e6c68429 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e14cb08bd081908af2120eb2925441 |
completed | April 16, 2026, 8:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffa14c1e508190a182db216cc4e326 |
completed | May 9, 2026, 9:04 p.m. |
Created at: April 10, 2026, 4:50 a.m.