Triple

T9035618
Position Surface form Disambiguated ID Type / Status
Subject Sikasso E216484 entity
Predicate connectedByRoadTo P11435 FINISHED
Object Korhogo E521812 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: Korhogo | Statement: [Sikasso, connectedByRoadTo, Korhogo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Korhogo
Context triple: [Sikasso, connectedByRoadTo, Korhogo]
  • A. Korhogo chosen
    Korhogo is a major city in northern Côte d'Ivoire that serves as an important cultural and economic center for the Senufo people.
  • B. Guéckédou
    Guéckédou is a town in southern Guinea known as a regional trading center near the borders with Sierra Leone and Liberia.
  • C. Daloa
    Daloa is a major inland city in western Côte d'Ivoire known as an important commercial and agricultural center, particularly for cocoa production.
  • D. Koulikoro
    Koulikoro is a town and region in southwestern Mali, situated along the Niger River and serving as an important administrative and transport hub.
  • E. Koudougou
    Koudougou is a major city in central Burkina Faso known as an important commercial and transportation hub.
  • 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_69ca83d10b608190b2b2f8e0a7faaf14 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cc6ac0b00c8190a7250b86bb7cc276 completed April 1, 2026, 12:45 a.m.
NED1 Entity disambiguation (via context triple) batch_69d047665204819090fe9c74fd64659e completed April 3, 2026, 11:04 p.m.
Created at: March 30, 2026, 7:08 p.m.