Triple
T23358610
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yawal |
E593120
|
entity |
| Predicate | vehicleRegistrationCode |
P1173
|
FINISHED |
| Object |
MH-19
MH-19 is the vehicle registration code assigned to the Yawal region in the Indian state of Maharashtra.
|
E1582582
|
NE FINISHED |
How this triple was built (4 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: MH-19 | Statement: [Yawal, vehicleRegistrationCode, MH-19]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MH-19 Context triple: [Yawal, vehicleRegistrationCode, MH-19]
-
A.
MH-11
MH-11 is the regional vehicle registration code assigned to motor vehicles registered in the Satara district of Maharashtra, India.
-
B.
MH-26
MH-26 is the regional vehicle registration code assigned to the Nanded district in the Indian state of Maharashtra.
-
C.
HM-1 Pantera helicopter
The HM-1 Pantera helicopter is a Brazilian-built, military variant of the Eurocopter AS365 Dauphin used primarily by the Brazilian Army for utility and transport missions.
-
D.
AH-6M
The AH-6M is a modernized variant of the U.S. Army’s light attack and reconnaissance helicopter, optimized for special operations missions with advanced avionics and weapons systems.
-
E.
Mil Mi-10
The Mil Mi-10 is a Soviet heavy-lift transport helicopter, derived from the Mi-6, notable for its long, spindly landing gear and ability to carry large external loads such as vehicles and prefabricated structures.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: MH-19 Triple: [Yawal, vehicleRegistrationCode, MH-19]
Generated description
MH-19 is the vehicle registration code assigned to the Yawal region in the Indian state of Maharashtra.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MH-19 Target entity description: MH-19 is the vehicle registration code assigned to the Yawal region in the Indian state of Maharashtra.
-
A.
MH-11
MH-11 is the regional vehicle registration code assigned to motor vehicles registered in the Satara district of Maharashtra, India.
-
B.
MH-26
MH-26 is the regional vehicle registration code assigned to the Nanded district in the Indian state of Maharashtra.
-
C.
HM-1 Pantera helicopter
The HM-1 Pantera helicopter is a Brazilian-built, military variant of the Eurocopter AS365 Dauphin used primarily by the Brazilian Army for utility and transport missions.
-
D.
AH-6M
The AH-6M is a modernized variant of the U.S. Army’s light attack and reconnaissance helicopter, optimized for special operations missions with advanced avionics and weapons systems.
-
E.
Mil Mi-10
The Mil Mi-10 is a Soviet heavy-lift transport helicopter, derived from the Mi-6, notable for its long, spindly landing gear and ability to carry large external loads such as vehicles and prefabricated structures.
- F. None of above. chosen
Provenance (5 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_69e25d24d2a4819092e6ede74c2a918d |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f19a196b308190bfe9bb4b6e7ec363 |
completed | April 29, 2026, 5:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c5dd385948190940cf5b878e54002 |
completed | May 19, 2026, 12:55 p.m. |
| NEDg | Description generation | batch_6a0c5fa0ae848190a23b0c8c16345c26 |
completed | May 19, 2026, 1:03 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c604cc84c8190946e2a35e0f736ce |
completed | May 19, 2026, 1:06 p.m. |
Created at: April 17, 2026, 5:29 p.m.