Triple

T8934638
Position Surface form Disambiguated ID Type / Status
Subject Southwestern Connecticut E212745 entity
Predicate contains P35 FINISHED
Object Darien E684061 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: Darien | Statement: [Southwestern Connecticut, contains, Darien]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Darien
Context triple: [Southwestern Connecticut, contains, Darien]
  • A. Darien chosen
    Darien is a coastal town in Fairfield County, Connecticut, known for its affluent residential character and location along Long Island Sound.
  • B. Demer
    Demer is a river in Belgium that flows through the provinces of Limburg and Flemish Brabant before joining the Dijle.
  • C. Wando
    Wando is a coastal city and island hub in South Jeolla Province, South Korea, known for its fisheries, seaweed production, and scenic maritime landscapes.
  • D. Bayaguana
    Bayaguana is a historic town and municipality in the Monte Plata province of the Dominican Republic, known for its religious traditions and rural agricultural character.
  • E. Guana Cay
    Guana Cay is a small, scenic Bahamian island known for its white-sand beaches, coral reefs, and laid-back resort and boating culture.
  • 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_69ca8395c438819087d7cb844ab5990c completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc669138b48190a6bb4968f029a69e completed April 1, 2026, 12:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfc1ddac548190bf520321dd35de1c completed April 3, 2026, 1:34 p.m.
Created at: March 30, 2026, 6:58 p.m.