Triple

T29263967
Position Surface form Disambiguated ID Type / Status
Subject Henry Clews E741924 entity
Predicate notableWork P4 FINISHED
Object Fifty Years in Wall Street
Fifty Years in Wall Street is a classic financial memoir in which banker and broker Henry Clews recounts his experiences, observations, and insider stories from half a century in the American securities markets.
 E1859559 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: Fifty Years in Wall Street | Statement: [Henry Clews, notableWork, Fifty Years in Wall Street]
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: Fifty Years in Wall Street
Triple: [Henry Clews, notableWork, Fifty Years in Wall Street]
Generated description
Fifty Years in Wall Street is a classic financial memoir in which banker and broker Henry Clews recounts his experiences, observations, and insider stories from half a century in the American securities markets.

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_69f0912065c08190bddd23e20e8ef18e completed April 28, 2026, 10:51 a.m.
NER Named-entity recognition batch_69f664dd47488190946f9a3f9c4a7d24 completed May 2, 2026, 8:55 p.m.
NED1 Entity disambiguation (via context triple) batch_6a25893271d48190b1b65ac878ca0508 completed June 7, 2026, 3:07 p.m.
NEDg Description generation batch_6a258dab84b081908f66170fc8e3151b completed June 7, 2026, 3:26 p.m.
NED2 Entity disambiguation (via description) batch_6a25919df80881909c817cf6b2c3ef56 completed June 7, 2026, 3:43 p.m.
Created at: April 28, 2026, 12:43 p.m.