Triple
T15250665
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meissen district |
E364508
|
entity |
| Predicate | vehicleRegistrationCode |
P1173
|
FINISHED |
| Object | MEI |
E364510
|
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: MEI | Statement: [Meissen district, vehicleRegistrationCode, MEI]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MEI Context triple: [Meissen district, vehicleRegistrationCode, MEI]
-
A.
MEI
MEI is a climate index that quantifies the strength and phase of the El Niño–Southern Oscillation by combining multiple atmospheric and oceanic variables over the tropical Pacific.
-
B.
MEI
chosen
MEI is the vehicle registration code for the German town of Meissen in the state of Saxony.
-
C.
MEI
MEI is the three-letter IATA airport code for Meridian Regional Airport in Meridian, Mississippi, United States.
-
D.
MEI
MEI is an abbreviation for Matsushita Electric Industrial Co., the Japanese electronics company better known globally by its Panasonic brand.
-
E.
MEIS
MEIS is a museum in Ferrara, Italy dedicated to exploring the history, culture, and experiences of Italian Judaism with a particular focus on the Holocaust.
- 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_69d85a0dde7481908fc64d1e82d5d20d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e007f62b9c8190b9ad40e2d1912b63 |
completed | April 15, 2026, 9:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fee5f184d481909eb4294ee3648226 |
completed | May 9, 2026, 7:44 a.m. |
Created at: April 10, 2026, 3:13 a.m.