Triple

T6739612
Position Surface form Disambiguated ID Type / Status
Subject BEY E154041 entity
Predicate airportServes P4363 FINISHED
Object Lebanon E10701 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: Lebanon | Statement: [BEY, airportServes, Lebanon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lebanon
Context triple: [BEY, airportServes, Lebanon]
  • A. Lebanon chosen
    Lebanon is a small Middle Eastern country on the eastern shore of the Mediterranean Sea, known for its rich history, diverse religious and cultural heritage, and historic capital, Beirut.
  • B. Tunisia
    Tunisia is a North African country on the Mediterranean coast, known for its strategic location, ancient Carthaginian and Roman heritage, and role as a key battleground in World War II.
  • C. Lebanon and Israel
    Lebanon and Israel are neighboring Middle Eastern countries with a long history of political conflict, military clashes, and unresolved territorial disputes.
  • D. Nabatieh, Lebanon
    Nabatieh is a predominantly Shia Muslim city in southern Lebanon known as a regional political and commercial center and for its major Ashura commemorations.
  • E. Circle of Lebanon
    Circle of Lebanon is a distinctive ring of Victorian-era catacombs built around a historic cedar tree in Highgate Cemetery in London.
  • 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_69c6880d84d8819095d19de2295f26ac completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d187c8788190b9fc1ebc9a66a520 completed March 27, 2026, 6:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69c70afa805c819098312fc0d06ec115 completed March 27, 2026, 10:55 p.m.
Created at: March 27, 2026, 2:10 p.m.