Triple

T27648580
Position Surface form Disambiguated ID Type / Status
Subject Frank S. Farley Service Plaza E696780 entity
Predicate namedAfter P63 FINISHED
Object Frank S. Farley
Frank S. Farley was a New Jersey state senator and influential political figure known for his long tenure and power in Atlantic County politics.
 E2297073 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: Frank S. Farley | Statement: [Frank S. Farley Service Plaza, namedAfter, Frank S. Farley]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Frank S. Farley
Triple: [Frank S. Farley Service Plaza, namedAfter, Frank S. Farley]
Generated description
Frank S. Farley was a New Jersey state senator and influential political figure known for his long tenure and power in Atlantic County politics.

Provenance (5 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_69ef590abd3c8190834d0193bde12007 completed April 27, 2026, 12:39 p.m.
NER Named-entity recognition batch_69f631d34c388190bdeb6f523856fc33 completed May 2, 2026, 5:18 p.m.
NED1 Entity disambiguation (via context triple) batch_6a82ff8602888190ad6365c036208d95 completed Aug. 17, 2026, 12:33 p.m.
NEDg Description generation batch_6a82ffd803748190a37e16a4f5a434ed completed Aug. 17, 2026, 12:34 p.m.
NED2 Entity disambiguation (via description) batch_6a8300e6b8988190a3c07a34eda62365 completed Aug. 17, 2026, 12:39 p.m.
Created at: April 27, 2026, 2:30 p.m.