Triple

T7794597
Position Surface form Disambiguated ID Type / Status
Subject Tabora E180266 entity
Predicate transport P230 FINISHED
Object Tabora Airport E177050 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: Tabora Airport | Statement: [Tabora, transport, Tabora Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tabora Airport
Context triple: [Tabora, transport, Tabora Airport]
  • A. Tabora Airport chosen
    Tabora Airport is a regional public airport serving the town and surrounding Tabora Region in western Tanzania.
  • B. Mpanda Airport
    Mpanda Airport is a regional airport in western Tanzania that serves the town of Mpanda and the surrounding Katavi Region.
  • C. Umbu Mehang Kunda Airport
    Umbu Mehang Kunda Airport is a regional airport serving the island of Sumba in East Nusa Tenggara, Indonesia, providing domestic connections to major Indonesian cities.
  • D. Kamuzu International Airport
    Kamuzu International Airport is the main international airport serving Lilongwe and one of the primary gateways to Malawi.
  • E. Kigoma Airport
    Kigoma Airport is a public airport in western Tanzania that serves the town of Kigoma on the shores of Lake Tanganyika.
  • 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_69ca827d22208190b4dc5aa680edcf5d completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cae93b262c8190b55e5ab2bc72d894 completed March 30, 2026, 9:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69cb13ea96cc819081ac26db3ecf4481 completed March 31, 2026, 12:23 a.m.
Created at: March 30, 2026, 4:31 p.m.