Triple

T16043874
Position Surface form Disambiguated ID Type / Status
Subject Badme region E389165 entity
Predicate hasSettlement P1068 FINISHED
Object Badme
Badme is a disputed border town between Ethiopia and Eritrea that became a focal point of their 1998–2000 war.
 E1191797 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: Badme | Statement: [Badme region, hasSettlement, Badme]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Badme
Context triple: [Badme region, hasSettlement, Badme]
  • A. Palhaça
    Palhaça is a civil parish in the municipality of Oliveira do Bairro, located in Portugal’s Aveiro District.
  • B. Manmadhudu
    Manmadhudu is a popular 2002 Telugu romantic comedy film starring Nagarjuna Akkineni, known for its witty humor and charming portrayal of a commitment-phobic ad executive.
  • C. Bajool
    Bajool is a small rural locality in Central Queensland, Australia, situated south of Rockhampton and known for its agricultural surroundings and proximity to transport routes.
  • D. Manmad
    Manmad is a major railway and commercial town in Maharashtra, India, known as an important junction connecting several key routes in the region.
  • E. Hosaena
    Hosaena is a town in southern Ethiopia that serves as an important administrative and commercial center in the Southern Nations, Nationalities, and Peoples' 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: Badme
Triple: [Badme region, hasSettlement, Badme]
Generated description
Badme is a disputed border town between Ethiopia and Eritrea that became a focal point of their 1998–2000 war.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Badme
Target entity description: Badme is a disputed border town between Ethiopia and Eritrea that became a focal point of their 1998–2000 war.
  • A. Palhaça
    Palhaça is a civil parish in the municipality of Oliveira do Bairro, located in Portugal’s Aveiro District.
  • B. Manmadhudu
    Manmadhudu is a popular 2002 Telugu romantic comedy film starring Nagarjuna Akkineni, known for its witty humor and charming portrayal of a commitment-phobic ad executive.
  • C. Bajool
    Bajool is a small rural locality in Central Queensland, Australia, situated south of Rockhampton and known for its agricultural surroundings and proximity to transport routes.
  • D. Manmad
    Manmad is a major railway and commercial town in Maharashtra, India, known as an important junction connecting several key routes in the region.
  • E. Hosaena
    Hosaena is a town in southern Ethiopia that serves as an important administrative and commercial center in the Southern Nations, Nationalities, and Peoples' 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_69d86dae698881908327ef2d67706cb9 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1835c5bd48190b5b47379bf51f84e completed April 17, 2026, 12:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffdbd95d508190a21db435fb69f8d7 completed May 10, 2026, 1:14 a.m.
NEDg Description generation batch_69ffde10adec81908c0b662780184131 completed May 10, 2026, 1:23 a.m.
NED2 Entity disambiguation (via description) batch_69ffde9037848190b8d3b84fdec93ed6 completed May 10, 2026, 1:25 a.m.
Created at: April 10, 2026, 4:56 a.m.