Triple

T396061
Position Surface form Disambiguated ID Type / Status
Subject Kai Bird E8984 entity
Predicate livedIn P75 FINISHED
Object Beirut E4667 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: Beirut | Statement: [Kai Bird, livedIn, Beirut]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Beirut
Context triple: [Kai Bird, livedIn, Beirut]
  • A. Beirut chosen
    Beirut is the capital and largest city of Lebanon, known as a historic cultural, commercial, and financial hub of the Eastern Mediterranean.
  • B. Tartus
    Tartus is a major Syrian port city on the Mediterranean coast that hosts Russia’s only naval facility outside the former Soviet Union.
  • C. Haifa
    Haifa is a major Israeli city on the Mediterranean coast, known for its significant port, mixed Jewish-Arab population, and the terraced Baháʼí Gardens on Mount Carmel.
  • D. Tunis
    Tunis is the capital and largest city of Tunisia, serving as a major political, economic, and cultural center in the Arab world.
  • E. Damascus
    Damascus is the capital and one of the largest cities of Syria, renowned as one of the oldest continuously inhabited cities in the world and a historic cultural and commercial center of the Arab world.
  • 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_69a2e7f55c60819097aff65ea2ca2832 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ec8a941081909a152fda0ce24a98 completed Feb. 28, 2026, 1:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69a413f1148481908b4a25355ee07069 completed March 1, 2026, 10:24 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.