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.