Triple

T15407734
Position Surface form Disambiguated ID Type / Status
Subject Abuja–Keffi–Lafia highway E368504 entity
Predicate passesNear P416 FINISHED
Object Masaka E1155181 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: Masaka | Statement: [Abuja–Keffi–Lafia highway, passesNear, Masaka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Masaka
Context triple: [Abuja–Keffi–Lafia highway, passesNear, Masaka]
  • A. Masaka chosen
    Masaka is a town located within the Karu Local Government Area in central Nigeria.
  • B. Kalangala
    Kalangala is a town on Uganda’s Ssese Islands in Lake Victoria, serving as the administrative and commercial center of Kalangala District.
  • C. Likasi
    Likasi is a mining city in the southeastern Democratic Republic of the Congo, known for its significant copper and cobalt production.
  • D. Masaka District
    Masaka District is an administrative district in southern Uganda known for its agricultural economy and its role as a key transport and commercial hub in the Central Region.
  • E. Kalangoya
    Kalangoya is an alternative name for the Kalanguya language, an Austronesian language spoken by indigenous communities in the northern Philippines.
  • 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_69d85a16c68c819099c1b547fbc87b32 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03ea36c6881909eaea48e9608897a completed April 16, 2026, 1:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff1a716248819094fd8b205cc2a3f2 completed May 9, 2026, 11:28 a.m.
Created at: April 10, 2026, 3:20 a.m.