Triple
T9171150
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monaghan |
E220081
|
entity |
| Predicate | hasVehicleRegistrationCode |
P1173
|
FINISHED |
| Object | MN |
E33799
|
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: MN | Statement: [Monaghan, hasVehicleRegistrationCode, MN]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MN Context triple: [Monaghan, hasVehicleRegistrationCode, MN]
-
A.
MN
MN is the vehicle registration code used on license plates for vehicles registered in Douglas and the rest of the Isle of Man.
-
B.
NM
NM is the standard two-letter U.S. postal abbreviation for the state of New Mexico.
-
C.
Minnesota
Minnesota is an American hip hop record producer known for his work with prominent rap artists in the 1990s and 2000s.
-
D.
Minnesota
Minnesota is an American electronic music producer and DJ known for his melodic, bass-heavy dubstep and festival performances.
-
E.
Minnesota
chosen
Minnesota is a U.S. state known for its numerous lakes, cold winters, and vibrant cultural and economic centers like Minneapolis–Saint Paul.
- 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_69ca83e467108190abcae6a33b3d4dad |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccaae38ee48190bf783477bc37913d |
completed | April 1, 2026, 5:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0549746b0819099c2a591900f9c8c |
completed | April 4, 2026, midnight |
Created at: March 30, 2026, 7:22 p.m.