Triple

T25676732

Position	Surface form	Disambiguated ID	Type / Status
Subject	Canon Hill	`E643825`	entity
Predicate	namedAfter	`P63`	FINISHED
Object	Col. John Canon Col. John Canon was an American Revolutionary-era military officer and early settler after whom the town of Canonsburg, Pennsylvania, and local landmarks such as Canon Hill are named.	`E1691698`	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: Col. John Canon | Statement: [Canon Hill, namedAfter, Col. John Canon]

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: Col. John Canon
Triple: [Canon Hill, namedAfter, Col. John Canon]

Generated description

Col. John Canon was an American Revolutionary-era military officer and early settler after whom the town of Canonsburg, Pennsylvania, and local landmarks such as Canon Hill are named.

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_69e77e7f69808190ad27df1006f6037a`	completed	April 21, 2026, 1:41 p.m.
NER	Named-entity recognition	`batch_69f5fb3712cc8190b6e851960cc7c9e9`	completed	May 2, 2026, 1:25 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a10c15d396881909a0825c45463dc38`	completed	May 22, 2026, 8:49 p.m.
NEDg	Description generation	`batch_6a10c1f3ae208190b3cdc518e83bbc7f`	completed	May 22, 2026, 8:52 p.m.
NED2	Entity disambiguation (via description)	`batch_6a10c2b0c540819086fe2b0fef3f76d1`	completed	May 22, 2026, 8:55 p.m.

Created at: April 21, 2026, 7:39 p.m.