Triple

T5012664
Position Surface form Disambiguated ID Type / Status
Subject Jeannette, Pennsylvania E112662 entity
Predicate nickname P55 FINISHED
Object Glass City E139116 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: Glass City | Statement: [Jeannette, Pennsylvania, nickname, Glass City]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Glass City
Context triple: [Jeannette, Pennsylvania, nickname, Glass City]
  • A. Glass City chosen
    Glass City is a nickname for Toledo, Ohio, reflecting its historic prominence in the glass manufacturing industry.
  • B. Glass City
    Glass City is a nickname commonly associated with Westland, likely referencing its historical or industrial ties to glass production or glass-related manufacturing.
  • C. River City
    River City is a popular nickname for Wuhan, a major central Chinese metropolis known for its location at the confluence of the Yangtze and Han rivers.
  • D. River City
    River City is a popular nickname for Sacramento, California, highlighting the city’s close connection to the nearby American and Sacramento Rivers.
  • E. River City
    River City is a common nickname and place name in the United States, often referring to cities situated along major rivers and popularized in American culture and media.
  • 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_69bd4434acb8819086679dbeccc2fe54 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd730f12a481908a27c15dc73987c6 completed March 20, 2026, 4:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69be926e5ef481909df3a4b9d793300a completed March 21, 2026, 12:43 p.m.
Created at: March 20, 2026, 1:35 p.m.