Triple

T10115970
Position Surface form Disambiguated ID Type / Status
Subject Perkins Eastman E218357 entity
Predicate hasOfficeIn P1268 FINISHED
Object Guayaquil E8988 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: Guayaquil | Statement: [Perkins Eastman, hasOfficeIn, Guayaquil]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Guayaquil
Context triple: [Perkins Eastman, hasOfficeIn, Guayaquil]
  • A. Guayaquil chosen
    Guayaquil is a major Pacific port city in southwestern Ecuador and the country’s principal commercial and industrial center.
  • B. Quito
    Quito is the high-altitude Andean city that serves as Ecuador’s political and cultural center, renowned for its well-preserved colonial historic center and dramatic mountain setting.
  • C. Esmeraldas
    Esmeraldas is a coastal city and province in northwestern Ecuador known for its Afro-Ecuadorian culture, beaches, and important oil and port industries.
  • D. Cumaná
    Cumaná is a historic coastal city in northeastern Venezuela, recognized as one of the oldest continuously inhabited European-founded settlements in the Americas.
  • E. Lima
    Lima is a station on Buenos Aires’ historic Underground Line A, serving passengers in the city’s central area.
  • 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_69ca83da93fc8190b54e44bc2b34857c completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cdd161831c81908bb3c77caa7c3ce1 completed April 2, 2026, 2:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2e5a9d77c8190892a0ae8b3f8e203 completed April 5, 2026, 10:43 p.m.
Created at: March 30, 2026, 9:04 p.m.