Triple

T932783
Position Surface form Disambiguated ID Type / Status
Subject Porbandar Airport E20129 entity
Predicate isInRegion P285 FINISHED
Object Saurashtra E15090 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: Saurashtra | Statement: [Porbandar Airport, isInRegion, Saurashtra]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Saurashtra
Context triple: [Porbandar Airport, isInRegion, Saurashtra]
  • A. Khandesh
    Khandesh is a historical region in northwestern Maharashtra, India, known for its distinct cultural identity and fertile agricultural plains along the Tapi River.
  • B. Konkan
    Konkan is a rugged, coastal region along the western shoreline of India, known for its distinctive culture, cuisine, and scenic Arabian Sea beaches.
  • C. Hadoti
    Hadoti is a cultural and geographic region in southeastern Rajasthan, India, known for its historic cities like Kota and Bundi and its distinctive Hadoti dialect of Rajasthani.
  • D. Gujarat chosen
    Gujarat is a western coastal state of India known for its significant role in trade and industry, rich cultural heritage, and historic cities such as Ahmedabad.
  • E. Marathwada
    Marathwada is a historically significant and predominantly rural region in central Maharashtra, India, known for its drought-prone agriculture, cultural heritage, and cities like Aurangabad.
  • 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_69a493af3dc48190adb7263e6e445ea1 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4b34c457c819085cbfa0c798cb4c6 completed March 1, 2026, 9:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac8f62a0c481909186e09aa914a029 completed March 7, 2026, 8:49 p.m.
Created at: March 1, 2026, 7:40 p.m.