Triple

T3596142
Position Surface form Disambiguated ID Type / Status
Subject Port of Mobile E76144 entity
Predicate serves P98 FINISHED
Object Georgia E14900 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: Georgia | Statement: [Port of Mobile, serves, Georgia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Georgia
Context triple: [Port of Mobile, serves, Georgia]
  • A. Georgia chosen
    Georgia is a southeastern U.S. state known for its diverse landscapes, historic cities like Atlanta and Savannah, and significant roles in both the Civil War and the civil rights movement.
  • B. Georgia
    Georgia is a country at the crossroads of Eastern Europe and Western Asia, known for its ancient culture, mountainous landscapes, and historic role along the Silk Road.
  • C. Georgia
    Georgia is a character from the musical and film "Burlesque," known for her role as one of the performers in the nightclub where the story unfolds.
  • D. Carolinas
    The Carolinas are a region of the southeastern United States comprising the states of North Carolina and South Carolina.
  • E. South Carolina
    South Carolina is a southeastern U.S. state known for its Atlantic coastline, historic cities like Charleston, and significant role in early American and Civil War history.
  • 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_69ad85d8042081908af94a04c410dec0 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc19b26bc8190b3cc0613a1a98361 completed March 8, 2026, 6:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69b402e8628c81909913974602782b7a completed March 13, 2026, 12:28 p.m.
Created at: March 8, 2026, 3:22 p.m.