Triple

T21661697
Position Surface form Disambiguated ID Type / Status
Subject Samir Geagea E534608 entity
Predicate regionOfActivity P82 FINISHED
Object Mount Lebanon NE NERFINISHED

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: Mount Lebanon | Statement: [Samir Geagea, regionOfActivity, Mount Lebanon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mount Lebanon
Context triple: [Samir Geagea, regionOfActivity, Mount Lebanon]
  • A. Mount Lebanon chosen
    Mount Lebanon is a historic mountainous region in modern-day Lebanon that has long served as a cultural and political heartland for the Druze community.
  • B. North Lebanon
    North Lebanon is a region in northern Lebanon that was a significant theater of conflict and political tension during the Lebanese Civil War.
  • C. Mt. Lebanon
    Mt. Lebanon is a suburban municipality just south of Pittsburgh, Pennsylvania, known for its residential neighborhoods, strong school system, and walkable business districts.
  • D. Mount Carmel
    Mount Carmel is a residential neighborhood in Hamden, Connecticut, known for its proximity to Sleeping Giant State Park and Quinnipiac University.
  • E. Mount Carmel
    Mount Carmel is a coastal mountain range in northern Israel known for its religious significance, scenic landscapes, and the city of Haifa built on its slopes.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0c467e1f48190af2650b19175abc4 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69ef6c0883d481908dfdc66832c34d74 completed April 27, 2026, 2 p.m.
Created at: April 16, 2026, 6:36 p.m.