Triple

T31216340
Position Surface form Disambiguated ID Type / Status
Subject Empirical studies of the New Jersey–Pennsylvania minimum wage experiment E795880 entity
Predicate criticizedBy P437 FINISHED
Object William Wascher
William Wascher is an American labor economist known for his influential research on minimum wage policy and its employment effects.
E2023295 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: William Wascher | Statement: [Empirical studies of the New Jersey–Pennsylvania minimum wage experiment, criticizedBy, William Wascher]
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: William Wascher
Triple: [Empirical studies of the New Jersey–Pennsylvania minimum wage experiment, criticizedBy, William Wascher]
Generated description
William Wascher is an American labor economist known for his influential research on minimum wage policy and its employment effects.

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_69f224d9d52c8190a61f68ded37fa755 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f69c2b9bd08190ba440c060ebef476 completed May 3, 2026, 12:51 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34b13e321481908d36eef7c863fb39 completed June 19, 2026, 3:02 a.m.
NEDg Description generation batch_6a34b1f2f6d4819082e910d0685eb95e completed June 19, 2026, 3:05 a.m.
NED2 Entity disambiguation (via description) batch_6a34b279c8688190b257df5ca22d7dd9 completed June 19, 2026, 3:07 a.m.
Created at: April 29, 2026, 9:10 p.m.