Triple

T20276340
Position Surface form Disambiguated ID Type / Status
Subject GA-02 E503025 entity
Predicate hasCity P316 FINISHED
Object Iron City, Georgia E443427 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: Iron City, Georgia | Statement: [GA-02, hasCity, Iron City, Georgia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Iron City, Georgia
Context triple: [GA-02, hasCity, Iron City, Georgia]
  • A. Iron City, Georgia chosen
    Iron City, Georgia is a small rural town located in Seminole County in the southwestern part of the state.
  • B. Mountain City, Georgia
    Mountain City, Georgia is a small town in the Appalachian foothills of northeastern Georgia known for its scenic mountain setting and outdoor recreation opportunities.
  • C. Argyle, Georgia
    Argyle, Georgia is a small rural town in southern Georgia known for its quiet community and location within Clinch County.
  • D. Lumber City, Georgia
    Lumber City, Georgia is a small rural city in Telfair County known historically as a timber and river trade community in southeastern Georgia.
  • E. Sylvania, Georgia
    Sylvania, Georgia is a small city in Screven County known as the county seat and a historic community in eastern Georgia.
  • 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_69e0b4b0e79c8190bd61f22ef1329fa8 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e675e3df68819096fb859bc92a0da1 completed April 20, 2026, 6:52 p.m.
NED1 Entity disambiguation (via context triple) batch_6a085a21233881908fb835541ae88c70 completed May 16, 2026, 11:50 a.m.
Created at: April 16, 2026, 10:32 a.m.