Triple

T1457520
Position Surface form Disambiguated ID Type / Status
Subject Quetta E31430 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object QAA
QAA is the vehicle registration code assigned to motor vehicles registered in Quetta, the capital city of Pakistan’s Balochistan province.
 E168059 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: QAA | Statement: [Quetta, vehicleRegistrationCode, QAA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: QAA
Context triple: [Quetta, vehicleRegistrationCode, QAA]
  • A. EQA
    EQA is Mercedes-Benz’s compact all-electric SUV designed as part of the brand’s EQ lineup of battery-powered vehicles.
  • B. QAR
    QAR is the Qatari riyal, the official national currency used in the State of Qatar.
  • C. QQS
    QQS is the IATA station code for London St Pancras International, a major central London railway terminus and international high-speed rail hub.
  • D. HAA
    HAA is the Harvard Alumni Association, the organization that connects and serves Harvard University’s global community of alumni.
  • E. QA
    QA is the two-letter ISO 3166-1 alpha-2 country code assigned to Qatar for international standardization and identification.
  • 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: QAA
Triple: [Quetta, vehicleRegistrationCode, QAA]
Generated description
QAA is the vehicle registration code assigned to motor vehicles registered in Quetta, the capital city of Pakistan’s Balochistan province.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: QAA
Target entity description: QAA is the vehicle registration code assigned to motor vehicles registered in Quetta, the capital city of Pakistan’s Balochistan province.
  • A. EQA
    EQA is Mercedes-Benz’s compact all-electric SUV designed as part of the brand’s EQ lineup of battery-powered vehicles.
  • B. QAR
    QAR is the Qatari riyal, the official national currency used in the State of Qatar.
  • C. QQS
    QQS is the IATA station code for London St Pancras International, a major central London railway terminus and international high-speed rail hub.
  • D. HAA
    HAA is the Harvard Alumni Association, the organization that connects and serves Harvard University’s global community of alumni.
  • E. QA
    QA is the two-letter ISO 3166-1 alpha-2 country code assigned to Qatar for international standardization and identification.
  • 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_69a49917dfc081909acdbdf5d684f1ef completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c59a462881908e84b27846a6bc04 completed March 1, 2026, 11:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad0e7643e081909a088035faf2022d completed March 8, 2026, 5:51 a.m.
NEDg Description generation batch_69ad121fee9c81909efddee10191b791 completed March 8, 2026, 6:07 a.m.
NED2 Entity disambiguation (via description) batch_69ad127f25548190bdbcf99132237ad4 completed March 8, 2026, 6:09 a.m.
Created at: March 1, 2026, 8 p.m.