Triple

T4307385
Position Surface form Disambiguated ID Type / Status
Subject FET E93987 entity
Predicate usedInCountry P715 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: [FET, usedInCountry, Lebanon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lebanon
Context triple: [FET, usedInCountry, 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. 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.
  • D. 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.
  • E. Syria
    Syria is a country in the Eastern Mediterranean region of Western Asia, known for its ancient civilizations, diverse cultural heritage, and protracted civil war since 2011.
  • 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_69b3451886588190a3dd1305ea7c58dc completed March 12, 2026, 10:58 p.m.
NER Named-entity recognition batch_69b350bb78cc8190a850aca47d8711cf completed March 12, 2026, 11:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5c756809c8190af90c91ec7883e55 completed March 14, 2026, 8:38 p.m.
Created at: March 12, 2026, 11:11 p.m.