Triple
T8728272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jowai |
E207184
|
entity |
| Predicate | vehicleRegistrationCode |
P1173
|
FINISHED |
| Object |
ML
ML is the vehicle registration code for the Indian state of Meghalaya, used on license plates including those registered in Jowai.
|
E753415
|
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: ML | Statement: [Jowai, vehicleRegistrationCode, ML]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ML Context triple: [Jowai, vehicleRegistrationCode, ML]
-
A.
ML
ML is a statically typed functional programming language developed at the University of Edinburgh, known for pioneering features like type inference, pattern matching, and modules that strongly influenced later languages such as Elm, Haskell, and OCaml.
-
B.
ML
ML is the postcode area in central Scotland that covers Motherwell and surrounding towns.
-
C.
ML
ML is a post-nominal honorific indicating a recipient of Papua New Guinea’s Order of Logohu, a national order of merit.
-
D.
MLE
MLE is the IATA airport code for Velana International Airport, the main international gateway to the Maldives located near the capital city Malé.
-
E.
ML-1
ML-1 is Pakistan Railways’ primary north–south main line, connecting major cities and serving as the backbone of the country’s rail transport system.
- 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: ML Triple: [Jowai, vehicleRegistrationCode, ML]
Generated description
ML is the vehicle registration code for the Indian state of Meghalaya, used on license plates including those registered in Jowai.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ML Target entity description: ML is the vehicle registration code for the Indian state of Meghalaya, used on license plates including those registered in Jowai.
-
A.
ML
ML is a statically typed functional programming language developed at the University of Edinburgh, known for pioneering features like type inference, pattern matching, and modules that strongly influenced later languages such as Elm, Haskell, and OCaml.
-
B.
ML
ML is the postcode area in central Scotland that covers Motherwell and surrounding towns.
-
C.
ML
ML is a post-nominal honorific indicating a recipient of Papua New Guinea’s Order of Logohu, a national order of merit.
-
D.
MLE
MLE is the IATA airport code for Velana International Airport, the main international gateway to the Maldives located near the capital city Malé.
-
E.
ML-1
ML-1 is Pakistan Railways’ primary north–south main line, connecting major cities and serving as the backbone of the country’s rail transport system.
- 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_69ca8358e4008190898471a59b96c301 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5d1890e0819088b271db51faa738 |
completed | March 31, 2026, 11:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf291b737481909a90e482273c5f76 |
completed | April 3, 2026, 2:42 a.m. |
| NEDg | Description generation | batch_69cf2bd42e6081908e016303eeb2241f |
completed | April 3, 2026, 2:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cf2ce47b748190b883063dc3e5d16b |
completed | April 3, 2026, 2:58 a.m. |
Created at: March 30, 2026, 6:37 p.m.