Triple

T13680414
Position Surface form Disambiguated ID Type / Status
Subject Orientale Province E327984 entity
Predicate containsCity P294 FINISHED
Object Bondo
Bondo is a town located in the northeastern part of the Democratic Republic of the Congo.
E1055538 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: Bondo | Statement: [Orientale Province, containsCity, Bondo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bondo
Context triple: [Orientale Province, containsCity, Bondo]
  • A. Bondo
    Bondo is a town in western Kenya’s Nyanza region, known as an administrative and commercial center near Lake Victoria.
  • B. Bostik
    Bostik is a global adhesive and sealant manufacturer known for its products used in construction, industrial, and consumer markets.
  • C. Rilsan
    Rilsan is a high-performance bio-based polyamide (nylon) material widely used for its durability, chemical resistance, and flexibility in demanding industrial applications.
  • D. Bondeno
    Bondeno is a municipality in northern Italy’s Emilia-Romagna region, known for its agricultural landscape and location within the Province of Ferrara.
  • E. Babbit
    Babbit is a cartoon character from the Looney Tunes universe, best known as one of the two mischievous cats who repeatedly try to catch Tweety Bird.
  • 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: Bondo
Triple: [Orientale Province, containsCity, Bondo]
Generated description
Bondo is a town located in the northeastern part of the Democratic Republic of the Congo.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Bondo
Target entity description: Bondo is a town located in the northeastern part of the Democratic Republic of the Congo.
  • A. Bondo
    Bondo is a town in western Kenya’s Nyanza region, known as an administrative and commercial center near Lake Victoria.
  • B. Bostik
    Bostik is a global adhesive and sealant manufacturer known for its products used in construction, industrial, and consumer markets.
  • C. Rilsan
    Rilsan is a high-performance bio-based polyamide (nylon) material widely used for its durability, chemical resistance, and flexibility in demanding industrial applications.
  • D. Bondeno
    Bondeno is a municipality in northern Italy’s Emilia-Romagna region, known for its agricultural landscape and location within the Province of Ferrara.
  • E. Babbit
    Babbit is a cartoon character from the Looney Tunes universe, best known as one of the two mischievous cats who repeatedly try to catch Tweety Bird.
  • 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_69d8076f1fa8819094664a59b55010df completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbc66cbb088190907cb89dda8e4ebd completed April 12, 2026, 4:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7944347a08190bc1386e78ddb3e71 completed May 3, 2026, 6:30 p.m.
NEDg Description generation batch_69f79523bf608190addeca563bea132e completed May 3, 2026, 6:34 p.m.
NED2 Entity disambiguation (via description) batch_69f7965cc9f88190acbf232615a9e87b completed May 3, 2026, 6:39 p.m.
Created at: April 9, 2026, 9:53 p.m.