Triple

T22617959
Position Surface form Disambiguated ID Type / Status
Subject Eastern Region of Ghana E558197 entity
Predicate contains P35 FINISHED
Object Kibi
Kibi is a town in southeastern Ghana known as the traditional capital of the Akyem Abuakwa state and a center of gold and bauxite mining.
 E1546718 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: Kibi | Statement: [Eastern Region of Ghana, contains, Kibi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kibi
Context triple: [Eastern Region of Ghana, contains, Kibi]
  • A. Kilana
    Kilana is a Vorta official serving the Dominion who appears in Star Trek: Deep Space Nine as a cunning and diplomatic antagonist.
  • B. Kilosa
    Kilosa is a town and district in eastern Tanzania known for its agricultural activities and location along the central railway in the Morogoro Region.
  • C. Kiso
    Kiso is a town in Nagano Prefecture, Japan, known for its scenic Kiso Valley, traditional post towns on the old Nakasendō route, and proximity to Mount Ontake.
  • D. Kibibi
    Kibibi is a lively and energetic host character in Disney's "Festival of the Lion King" stage show at Disney theme parks.
  • E. Kile
    Kile is a KDE-based integrated LaTeX editor that provides tools for writing, compiling, and previewing LaTeX documents efficiently.
  • 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: Kibi
Triple: [Eastern Region of Ghana, contains, Kibi]
Generated description
Kibi is a town in southeastern Ghana known as the traditional capital of the Akyem Abuakwa state and a center of gold and bauxite mining.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kibi
Target entity description: Kibi is a town in southeastern Ghana known as the traditional capital of the Akyem Abuakwa state and a center of gold and bauxite mining.
  • A. Kilana
    Kilana is a Vorta official serving the Dominion who appears in Star Trek: Deep Space Nine as a cunning and diplomatic antagonist.
  • B. Kilosa
    Kilosa is a town and district in eastern Tanzania known for its agricultural activities and location along the central railway in the Morogoro Region.
  • C. Kiso
    Kiso is a town in Nagano Prefecture, Japan, known for its scenic Kiso Valley, traditional post towns on the old Nakasendō route, and proximity to Mount Ontake.
  • D. Kibibi
    Kibibi is a lively and energetic host character in Disney's "Festival of the Lion King" stage show at Disney theme parks.
  • E. Kile
    Kile is a KDE-based integrated LaTeX editor that provides tools for writing, compiling, and previewing LaTeX documents efficiently.
  • 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_69e24545a8e08190bfa7482a2c725ff1 completed April 17, 2026, 2:35 p.m.
NER Named-entity recognition batch_69f167ef7a148190870334af9c8b79a4 completed April 29, 2026, 2:07 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0b4e018e04819086ec5d885fb76f67 completed May 18, 2026, 5:36 p.m.
NEDg Description generation batch_6a0b513c2908819087b05efdbf8688aa completed May 18, 2026, 5:49 p.m.
NED2 Entity disambiguation (via description) batch_6a0b51db01948190b968a3f289419776 completed May 18, 2026, 5:52 p.m.
Created at: April 17, 2026, 2:59 p.m.