Triple

T12739906
Position Surface form Disambiguated ID Type / Status
Subject Scioa E304461 entity
Predicate borders P224 FINISHED
Object Hararghe E603973 NE FINISHED

How this triple was built (2 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: Hararghe | Statement: [Scioa, borders, Hararghe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hararghe
Context triple: [Scioa, borders, Hararghe]
  • A. Hararghe chosen
    Hararghe is a historical region in eastern Ethiopia known for its Oromo population, coffee production, and role as a former administrative province.
  • B. Hamra
    Hamra is a vibrant, cosmopolitan neighborhood in Beirut, Lebanon, known for its bustling commercial streets, cafes, and cultural life.
  • C. Thoseghar
    Thoseghar is a small village in Maharashtra, India, known primarily as the access point to the scenic Thoseghar Waterfalls and surrounding hilly countryside.
  • D. Metehara
    Metehara is a town in central Ethiopia known for its sugar plantations and proximity to both the Awash National Park and Lake Basaka.
  • E. Alto de Arkale
    Alto de Arkale is a short but steep climb in the Basque Country that frequently features as a decisive ascent in the Clásica de San Sebastián professional cycling race.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d7bdf1426c8190a4402e1c4cdec33a completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d9646dfc908190bc398935d1d23537 completed April 10, 2026, 8:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69f67c8ff57c8190a935b5c9f4bb5aa3 completed May 2, 2026, 10:37 p.m.
Created at: April 9, 2026, 5:26 p.m.