Triple

T3025973
Position Surface form Disambiguated ID Type / Status
Subject Colombo–Matara railway line E82574 entity
Predicate connects P390 FINISHED
Object Matara E323360 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: Matara | Statement: [Colombo–Matara railway line, connects, Matara]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Matara
Context triple: [Colombo–Matara railway line, connects, Matara]
  • A. Matara chosen
    Matara is a major coastal city in southern Sri Lanka known for its historic fort, beaches, and role as a regional commercial and transport hub.
  • B. Matara District
    Matara District is an administrative district in southern Sri Lanka known for its coastal cities, historical sites, and agricultural hinterland.
  • C. Unawatuna
    Unawatuna is a popular coastal town in southern Sri Lanka known for its palm-fringed beach, coral-rich bay, and laid-back tourist atmosphere.
  • D. Hambantota
    Hambantota is a coastal city in southern Sri Lanka known for its rapid development, including major infrastructure projects like a deep-sea port and international airport.
  • E. Yala
    Yala is a language spoken by the Yala people of Cross River State in southeastern Nigeria.
  • 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_69ad8b1fb34081908c1b873e2b7273e1 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad9abc78b48190a5283e7407a78fe7 completed March 8, 2026, 3:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2034727808190b016223c8ac911c3 completed March 12, 2026, 12:05 a.m.
Created at: March 8, 2026, 3 p.m.