Triple

T4488119
Position Surface form Disambiguated ID Type / Status
Subject David M. Satterfield E107298 entity
Predicate workLocation P7 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: [David M. Satterfield, workLocation, Beirut]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Beirut
Context triple: [David M. Satterfield, workLocation, 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. Jounieh
    Jounieh is a coastal city in Lebanon known for its seaside resorts, vibrant nightlife, and proximity to the historic pilgrimage site of Harissa.
  • C. Tartus
    Tartus is a major Syrian port city on the Mediterranean coast that hosts Russia’s only naval facility outside the former Soviet Union.
  • D. Port of Beirut
    The Port of Beirut is Lebanon’s principal maritime gateway and commercial hub on the Mediterranean Sea, historically central to the country’s trade and economy.
  • E. Aleppo
    Aleppo is an ancient and historically significant city in northern Syria, renowned for its rich cultural heritage, medieval architecture, and role as a major trading hub along the Silk Road.
  • 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_69bd43f84f788190a1383579c4a595be completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd52abddf88190b4fb09884ed62500 completed March 20, 2026, 1:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69bd67a31d0c819089954c8af4b1bdd6 completed March 20, 2026, 3:28 p.m.
Created at: March 20, 2026, 12:59 p.m.