Triple

T13917372
Position Surface form Disambiguated ID Type / Status
Subject Kesh E334656 entity
Predicate knownAs P39 FINISHED
Object Green City E488162 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: Green City | Statement: [Kesh, knownAs, Green City]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Green City
Context triple: [Kesh, knownAs, Green City]
  • A. Green City chosen
    Green City is a lush, verdant urban area known for its abundant greenery and natural landscapes.
  • B. Clean City
    Clean City is a popular nickname for Rajshahi, a major city in western Bangladesh known for its cleanliness and greenery.
  • C. Circular City
    Circular City is a historic walled island-terrace within Beijing’s Beihai Park, known for its ancient pavilions, stone carvings, and scenic views over the surrounding lake.
  • D. Green City in the Sun
    Green City in the Sun is a popular nickname for Nairobi, highlighting the Kenyan capital’s lush greenery and warm, sunny climate.
  • E. Sustainability District
    Sustainability District is one of Expo 2020 Dubai’s main themed zones, showcasing innovations, pavilions, and experiences focused on environmental stewardship and sustainable development.
  • 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_69d81c5f739081908bc05b2461f54828 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de272753e48190bc609482635280ff completed April 14, 2026, 11:38 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7ce7a1c388190a57dfdbbb732bbcb completed May 3, 2026, 10:38 p.m.
Created at: April 9, 2026, 10:16 p.m.