Triple
T7035826
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prenzlau |
E163379
|
entity |
| Predicate | vehicleRegistrationCode |
P1173
|
FINISHED |
| Object | UM |
E120686
|
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: UM | Statement: [Prenzlau, vehicleRegistrationCode, UM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: UM Context triple: [Prenzlau, vehicleRegistrationCode, UM]
-
A.
UM
UM is the stock ticker symbol for MRU, the Canadian food and pharmacy retail company Metro Inc.
-
B.
UM
UM is the commonly used abbreviation for Universitas Negeri Malang, a public university in Malang, Indonesia known for its strong focus on education and teacher training.
-
C.
UM
UM is the commonly used abbreviation for the University of Miami, a private research university located in Coral Gables, Florida.
-
D.
UM
chosen
UM is the regional vehicle registration code used for the district of Uckermark in the German state of Brandenburg.
-
E.
UM
UM is a public research university in Winnipeg, Canada, known as the University of Manitoba.
- 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_69c6885e7c1c8190be32a8f79ab4e0cf |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e220508c8190b8950cf38280b8c2 |
completed | March 27, 2026, 8:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c775a211f88190afe5ed466abcac7a |
completed | March 28, 2026, 6:30 a.m. |
Created at: March 27, 2026, 2:36 p.m.