Triple

T10432798
Position Surface form Disambiguated ID Type / Status
Subject Lambaréné E245957 entity
Predicate roadConnectionTo P9041 FINISHED
Object Port-Gentil E258556 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: Port-Gentil | Statement: [Lambaréné, roadConnectionTo, Port-Gentil]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Port-Gentil
Context triple: [Lambaréné, roadConnectionTo, Port-Gentil]
  • A. Port-Gentil chosen
    Port-Gentil is Gabon's second-largest city and a major oil and port hub located on the country's Atlantic coast.
  • B. Limbé
    Limbé is a historic town in northern Haiti known for its agricultural surroundings and role in the country’s colonial and revolutionary past.
  • C. Pointe-Noire
    Pointe-Noire is a major port city on the Atlantic coast of the Republic of the Congo and one of the country’s principal economic and industrial centers.
  • D. Libreville
    Libreville is the largest city and main economic and cultural center of Gabon, located on the country’s Atlantic coast.
  • E. Douala
    Douala is the economic capital and main port city of Cameroon, located on the Wouri River along the Atlantic coast.
  • 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_69d381bf3dc08190bf35a2643e4e8f22 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4ea64f12c81909861d0d5165da2a2 completed April 7, 2026, 11:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69e525100e108190b4f6949695c7156e completed April 19, 2026, 6:55 p.m.
Created at: April 6, 2026, 12:13 p.m.