Triple

T19324416
Position Surface form Disambiguated ID Type / Status
Subject Township of Woodbridge E483310 entity
Predicate contains P35 FINISHED
Object Port Reading NE NERFINISHED

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: Port Reading | Statement: [Township of Woodbridge, contains, Port Reading]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Port Reading
Context triple: [Township of Woodbridge, contains, Port Reading]
  • A. Port Reading, New Jersey chosen
    Port Reading, New Jersey is an industrial and residential community in Woodbridge Township known historically as a rail and shipping hub along the Arthur Kill.
  • B. Fair Haven
    Fair Haven is a small town in western Vermont known for its historic village green and slate industry heritage.
  • C. Fair Haven
    Fair Haven is a historic, traditionally working-class waterfront neighborhood in eastern New Haven, Connecticut, known for its diverse community and maritime roots along the Quinnipiac River.
  • D. Port Jersey
    Port Jersey is a major maritime shipping and industrial port facility on the New Jersey side of New York Harbor, serving as a key hub for containerized cargo and logistics.
  • E. Wilkesport
    Wilkesport is a small community located within St. Clair Township in southwestern Ontario, Canada.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e8d13e3c81909d91d1d5ec37c095 completed April 10, 2026, 12:10 p.m.
NER Named-entity recognition batch_69e60d8af43c81908e5a8780c35a8e1d completed April 20, 2026, 11:27 a.m.
Created at: April 10, 2026, 1:32 p.m.