Triple

T2740929
Position Surface form Disambiguated ID Type / Status
Subject Ndowe E60746 entity
Predicate neighboringLanguages P16383 FINISHED
Object Kombe
Kombe is a Bantu language spoken by the Kombe people of coastal Equatorial Guinea and nearby regions, closely related to other Ndowe-area languages.
 E295925 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: Kombe | Statement: [Ndowe, neighboringLanguages, Kombe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kombe
Context triple: [Ndowe, neighboringLanguages, Kombe]
  • A. Komaba
    Komaba is a district in Meguro, Tokyo, best known as the location of the University of Tokyo’s Komaba Campus and its surrounding academic and residential facilities.
  • B. Negombo
    Negombo is a coastal city in western Sri Lanka known historically as a strategic colonial port and today for its fishing industry and beach tourism.
  • C. Kiyombe
    Kiyombe is a regional variety of the Kikongo language spoken by communities in parts of Central Africa.
  • D. Koutiala
    Koutiala is a major city in southern Mali known as an important center for cotton production and agriculture.
  • E. Kumba
    Kumba is a renowned steel roller coaster at Busch Gardens Tampa Bay, famous for its intense inversions and smooth, high-speed layout.
  • 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: Kombe
Triple: [Ndowe, neighboringLanguages, Kombe]
Generated description
Kombe is a Bantu language spoken by the Kombe people of coastal Equatorial Guinea and nearby regions, closely related to other Ndowe-area languages.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kombe
Target entity description: Kombe is a Bantu language spoken by the Kombe people of coastal Equatorial Guinea and nearby regions, closely related to other Ndowe-area languages.
  • A. Komaba
    Komaba is a district in Meguro, Tokyo, best known as the location of the University of Tokyo’s Komaba Campus and its surrounding academic and residential facilities.
  • B. Negombo
    Negombo is a coastal city in western Sri Lanka known historically as a strategic colonial port and today for its fishing industry and beach tourism.
  • C. Kiyombe
    Kiyombe is a regional variety of the Kikongo language spoken by communities in parts of Central Africa.
  • D. Koutiala
    Koutiala is a major city in southern Mali known as an important center for cotton production and agriculture.
  • E. Kumba
    Kumba is a renowned steel roller coaster at Busch Gardens Tampa Bay, famous for its intense inversions and smooth, high-speed layout.
  • 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_69ab4b77febc819095603eb012cd141b completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abdb2f210881909126307cc92ebfef completed March 7, 2026, 8 a.m.
NED1 Entity disambiguation (via context triple) batch_69afbbca8ac081909ce86d4cd911b4f1 completed March 10, 2026, 6:35 a.m.
NEDg Description generation batch_69afbcc1dd988190826ab05e55adf1ee completed March 10, 2026, 6:40 a.m.
NED2 Entity disambiguation (via description) batch_69afbd452e1c8190a3ee9eaf642e80a0 completed March 10, 2026, 6:42 a.m.
Created at: March 6, 2026, 9:56 p.m.