Triple

T32800197
Position Surface form Disambiguated ID Type / Status
Subject ME to WE social enterprise initiatives E838874 entity
Predicate includes P1393 FINISHED
Object ME to WE Artisans
ME to WE Artisans is a fair-trade, socially conscious jewelry and accessories line that empowers artisans in developing communities through sustainable income and ethical production.
E2022760 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: ME to WE Artisans | Statement: [ME to WE social enterprise initiatives, includes, ME to WE Artisans]
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: ME to WE Artisans
Triple: [ME to WE social enterprise initiatives, includes, ME to WE Artisans]
Generated description
ME to WE Artisans is a fair-trade, socially conscious jewelry and accessories line that empowers artisans in developing communities through sustainable income and ethical production.

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_69f3493c7f6881908edf2aa13631d1e0 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6cd9ca70c81908c47d9f8adee301a completed May 3, 2026, 4:22 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34b1726abc81908bdf45a3fcaf0f99 completed June 19, 2026, 3:03 a.m.
NEDg Description generation batch_6a34b2507fa48190873d80197bc0eefc completed June 19, 2026, 3:06 a.m.
NED2 Entity disambiguation (via description) batch_6a34b2e5c568819099352545177c81b7 completed June 19, 2026, 3:09 a.m.
Created at: May 1, 2026, 1:14 a.m.