Triple

T33147451
Position Surface form Disambiguated ID Type / Status
Subject Buckingham Township, Bucks County, Pennsylvania E848340 entity
Predicate contains P35 FINISHED
Object Buckingham Mountain
Buckingham Mountain is a prominent natural elevation in Bucks County, Pennsylvania, known for its scenic views and rural, wooded landscape.
E2287481 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: Buckingham Mountain | Statement: [Buckingham Township, Bucks County, Pennsylvania, contains, Buckingham Mountain]
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: Buckingham Mountain
Triple: [Buckingham Township, Bucks County, Pennsylvania, contains, Buckingham Mountain]
Generated description
Buckingham Mountain is a prominent natural elevation in Bucks County, Pennsylvania, known for its scenic views and rural, wooded landscape.

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_69f3495a458c8190a1d34b237ba0be3f completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6d88ffa6081909b64a7014108abc7 completed May 3, 2026, 5:09 a.m.
NED1 Entity disambiguation (via context triple) batch_6a59f095473881909bb4bd53bad91ea2 completed July 17, 2026, 9:06 a.m.
NEDg Description generation batch_6a59f2942b54819087964ecd1871b04b completed July 17, 2026, 9:15 a.m.
NED2 Entity disambiguation (via description) batch_6a59f3800a048190a379c6aa68e86f2c completed July 17, 2026, 9:18 a.m.
Created at: May 1, 2026, 1:28 a.m.