Triple

T9193312
Position Surface form Disambiguated ID Type / Status
Subject Hissène Habré E220641 entity
Predicate familyName P18 FINISHED
Object Habré
Habré is the surname of Hissène Habré, the former president of Chad who was later convicted of crimes against humanity.
 E783223 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: Habré | Statement: [Hissène Habré, familyName, Habré]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Habré
Context triple: [Hissène Habré, familyName, Habré]
  • A. Diez
    Diez is a small historic town in western Germany’s Rhineland-Palatinate, known for its picturesque setting on the Lahn River and its prominent hilltop castle.
  • B. Haría
    Haría is a picturesque municipality and village in the northern part of Lanzarote in Spain’s Canary Islands, known for its lush “Valley of a Thousand Palms” and traditional architecture.
  • C. Hanno
    Hanno is a city in Saitama Prefecture, Japan, known for its natural scenery, hiking spots, and proximity to the Tokyo metropolitan area.
  • D. Pero
    Pero is a common South Slavic diminutive form of the male given name Petar (Peter).
  • E. Pero
    Pero is a West Chadic language spoken in parts of Nigeria.
  • 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: Habré
Triple: [Hissène Habré, familyName, Habré]
Generated description
Habré is the surname of Hissène Habré, the former president of Chad who was later convicted of crimes against humanity.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Habré
Target entity description: Habré is the surname of Hissène Habré, the former president of Chad who was later convicted of crimes against humanity.
  • A. Diez
    Diez is a small historic town in western Germany’s Rhineland-Palatinate, known for its picturesque setting on the Lahn River and its prominent hilltop castle.
  • B. Haría
    Haría is a picturesque municipality and village in the northern part of Lanzarote in Spain’s Canary Islands, known for its lush “Valley of a Thousand Palms” and traditional architecture.
  • C. Hanno
    Hanno is a city in Saitama Prefecture, Japan, known for its natural scenery, hiking spots, and proximity to the Tokyo metropolitan area.
  • D. Pero
    Pero is a common South Slavic diminutive form of the male given name Petar (Peter).
  • E. Pero
    Pero is a West Chadic language spoken in parts of Nigeria.
  • 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_69ca83e7ba70819088b74866d9da2c30 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69ccd5c1fa9c8190bc5cc6dce8778694 completed April 1, 2026, 8:22 a.m.
NED1 Entity disambiguation (via context triple) batch_69d05c313004819090fe0e5d4e7bc15e completed April 4, 2026, 12:32 a.m.
NEDg Description generation batch_69d05d138c288190a0eab9be6bd649c0 completed April 4, 2026, 12:36 a.m.
NED2 Entity disambiguation (via description) batch_69d05df1a0888190a2bdc48a159b865e completed April 4, 2026, 12:40 a.m.
Created at: March 30, 2026, 7:24 p.m.