Triple

T36318486
Position Surface form Disambiguated ID Type / Status
Subject Danville, Pennsylvania E894263 entity
Predicate hasLandmark P105 FINISHED
Object Montour County Courthouse
Montour County Courthouse is a historic governmental building serving as the judicial center of Montour County in Danville, Pennsylvania.
E2178243 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: Montour County Courthouse | Statement: [Danville, Pennsylvania, hasLandmark, Montour County Courthouse]
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: Montour County Courthouse
Triple: [Danville, Pennsylvania, hasLandmark, Montour County Courthouse]
Generated description
Montour County Courthouse is a historic governmental building serving as the judicial center of Montour County in Danville, Pennsylvania.

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_69f76e4d1a788190a6ab6ccca28547a7 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7ba42d4388190bd16f0c069184439 completed May 3, 2026, 9:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a397d8e25448190b5538da8f4490f20 completed June 22, 2026, 6:23 p.m.
NEDg Description generation batch_6a398004d48081909ee84522beb0d8d2 completed June 22, 2026, 6:33 p.m.
NED2 Entity disambiguation (via description) batch_6a3980900c788190872cd7de16c8694c completed June 22, 2026, 6:36 p.m.
Created at: May 3, 2026, 4:09 p.m.