Triple

T1595349
Position Surface form Disambiguated ID Type / Status
Subject Bioko Island E34267 entity
Predicate hasCity P316 FINISHED
Object Luba
Luba is a coastal town and important port on the southern part of Bioko Island in Equatorial Guinea.
 E184004 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: Luba | Statement: [Bioko Island, hasCity, Luba]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Luba
Context triple: [Bioko Island, hasCity, Luba]
  • A. Tshiluba
    Tshiluba is a Bantu language widely spoken in south-central Democratic Republic of the Congo, particularly in the Kasai region.
  • B. Ngoni
    Ngoni is a Bantu language spoken by the Ngoni people of parts of Malawi, Tanzania, Mozambique, and Zambia, reflecting historical migrations from the Zulu region.
  • C. Lozi
    Lozi is a Bantu language spoken primarily by the Lozi people in western Zambia and surrounding regions of southern Africa.
  • D. Kibondo
    Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
  • E. Ndau
    Ndau is a Southern Bantu language spoken primarily in central Mozambique and eastern Zimbabwe, closely related to Shona.
  • 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: Luba
Triple: [Bioko Island, hasCity, Luba]
Generated description
Luba is a coastal town and important port on the southern part of Bioko Island in Equatorial Guinea.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Luba
Target entity description: Luba is a coastal town and important port on the southern part of Bioko Island in Equatorial Guinea.
  • A. Tshiluba
    Tshiluba is a Bantu language widely spoken in south-central Democratic Republic of the Congo, particularly in the Kasai region.
  • B. Ngoni
    Ngoni is a Bantu language spoken by the Ngoni people of parts of Malawi, Tanzania, Mozambique, and Zambia, reflecting historical migrations from the Zulu region.
  • C. Lozi
    Lozi is a Bantu language spoken primarily by the Lozi people in western Zambia and surrounding regions of southern Africa.
  • D. Kibondo
    Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
  • E. Ndau
    Ndau is a Southern Bantu language spoken primarily in central Mozambique and eastern Zimbabwe, closely related to Shona.
  • 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_69a885fdcb9c819081ce6f0b8cd477dd completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a9092ccb388190b2f3ed86b3853651 completed March 5, 2026, 4:40 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad58c1d9ac819085e497b630a99d96 completed March 8, 2026, 11:08 a.m.
NEDg Description generation batch_69ad59f12e908190a1cec5f1fc4149d3 completed March 8, 2026, 11:13 a.m.
NED2 Entity disambiguation (via description) batch_69ad5a94c1488190a3853ffb8683d69c completed March 8, 2026, 11:16 a.m.
Created at: March 4, 2026, 7:27 p.m.