Triple

T27541735

Position	Surface form	Disambiguated ID	Type / Status
Subject	史記	`E695253`	entity
Predicate	hasPart	`P35`	FINISHED
Object	貨殖列傳貨殖列傳 is a chapter of Sima Qian’s historical masterpiece Shiji that focuses on the biographies and activities of merchants and the accumulation of wealth in ancient China.	`E695253`	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: 貨殖列傳 | Statement: [史記, hasPart, 貨殖列傳]

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: 貨殖列傳
Triple: [史記, hasPart, 貨殖列傳]

Generated description

貨殖列傳 is a chapter of Sima Qian’s historical masterpiece Shiji that focuses on the biographies and activities of merchants and the accumulation of wealth in ancient China.

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_69ef5386c3e08190bfe33aa326e1f72b`	completed	April 27, 2026, 12:16 p.m.
NER	Named-entity recognition	`batch_69f62f5ec8b481909241271f7d602dc9`	completed	May 2, 2026, 5:07 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a12c5c0f60c8190b0507205313d9a59`	completed	May 24, 2026, 9:32 a.m.
NEDg	Description generation	`batch_6a12c6e244108190940a87f7158f4912`	completed	May 24, 2026, 9:37 a.m.
NED2	Entity disambiguation (via description)	`batch_6a12cb0f77248190a107c7cc99767cf6`	completed	May 24, 2026, 9:55 a.m.

Created at: April 27, 2026, 1:31 p.m.