Triple

T9199910
Position Surface form Disambiguated ID Type / Status
Subject Middle Guinea E220809 entity
Predicate hasMajorCity P316 FINISHED
Object Lélouma
Lélouma is a significant urban center in the Middle Guinea region of Guinea, serving as an important local hub for administration and commerce.
 E784220 NE FINISHED

How this triple was built (4 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: Lélouma | Statement: [Middle Guinea, hasMajorCity, Lélouma]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lélouma
Context triple: [Middle Guinea, hasMajorCity, Lélouma]
  • A. Langoué Baï
    Langoué Baï is a renowned forest clearing in Gabon celebrated for its rich biodiversity and frequent gatherings of forest elephants and other wildlife.
  • B. Bittou
    Bittou is a town in Burkina Faso known for its role as a regional trading center and its international town-twinning links with European municipalities.
  • C. Mundemba
    Mundemba is a town in southwestern Cameroon known as a gateway to the biodiverse Korup National Park.
  • D. Tanguy
    Tanguy is a French surname most notably associated with Yves Tanguy, a prominent 20th-century Surrealist painter.
  • E. Makouda
    Makouda is a town and commune located in northern Algeria within the Kabylie region.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Lélouma
Triple: [Middle Guinea, hasMajorCity, Lélouma]
Generated description
Lélouma is a significant urban center in the Middle Guinea region of Guinea, serving as an important local hub for administration and commerce.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lélouma
Target entity description: Lélouma is a significant urban center in the Middle Guinea region of Guinea, serving as an important local hub for administration and commerce.
  • A. Langoué Baï
    Langoué Baï is a renowned forest clearing in Gabon celebrated for its rich biodiversity and frequent gatherings of forest elephants and other wildlife.
  • B. Bittou
    Bittou is a town in Burkina Faso known for its role as a regional trading center and its international town-twinning links with European municipalities.
  • C. Mundemba
    Mundemba is a town in southwestern Cameroon known as a gateway to the biodiverse Korup National Park.
  • D. Tanguy
    Tanguy is a French surname most notably associated with Yves Tanguy, a prominent 20th-century Surrealist painter.
  • E. Makouda
    Makouda is a town and commune located in northern Algeria within the Kabylie region.
  • F. None of above. chosen

Provenance (5 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_69ca83e8e9248190862cf3e41693b310 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69ccd881c5d48190bbc33fac71a1d849 completed April 1, 2026, 8:34 a.m.
NED1 Entity disambiguation (via context triple) batch_69d05c451210819091188151d799e4cb completed April 4, 2026, 12:33 a.m.
NEDg Description generation batch_69d05dbbe0d08190a15107c17948167f completed April 4, 2026, 12:39 a.m.
NED2 Entity disambiguation (via description) batch_69d05e34ae6881908172770c6097a5e5 completed April 4, 2026, 12:41 a.m.
Created at: March 30, 2026, 7:25 p.m.