Triple

T10882494
Position Surface form Disambiguated ID Type / Status
Subject Arkhangelsk Talagi Airport E256958 entity
Predicate cityServed P82 FINISHED
Object Arkhangelsk E101428 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: Arkhangelsk | Statement: [Arkhangelsk Talagi Airport, cityServed, Arkhangelsk]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Arkhangelsk
Context triple: [Arkhangelsk Talagi Airport, cityServed, Arkhangelsk]
  • A. Arkhangelsk chosen
    Arkhangelsk is a historic port city in northern Russia on the White Sea, long serving as a key maritime gateway and administrative center of the surrounding region.
  • B. Archangelskoye
    Archangelskoye is a historic estate and former aristocratic residence near Moscow, Russia, known for its neoclassical palace, landscaped park, and role as a cultural and political retreat.
  • C. Murmansk
    Murmansk is a major Arctic port city in northwestern Russia, known for its ice-free harbor and strategic military and shipping importance.
  • D. Severodvinsk
    Severodvinsk is a Russian port city on the White Sea, known as a major center for the construction and maintenance of nuclear submarines.
  • E. Tomsk
    Tomsk is a historic university and research city in southwestern Siberia, known as one of the region’s oldest and most important cultural and educational centers.
  • 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_69d6aa848804819081b2713ca0bedf06 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d751db24208190b3a7ed7eea118522 completed April 9, 2026, 7:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69e373f38cfc8190977d1a59c31dac5f completed April 18, 2026, 12:07 p.m.
Created at: April 8, 2026, 9:21 p.m.