Triple

T25651124
Position Surface form Disambiguated ID Type / Status
Subject Isabella Van Wagenen E643100 entity
Predicate notableWork P4 FINISHED
Object “Ain’t I a Woman?” speech
“Ain’t I a Woman?” is the famous 1851 speech by abolitionist and women’s rights activist Sojourner Truth that powerfully challenged prevailing notions of racial and gender inferiority and inequality.
 E1689148 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: “Ain’t I a Woman?” speech | Statement: [Isabella Van Wagenen, notableWork, “Ain’t I a Woman?” speech]
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: “Ain’t I a Woman?” speech
Triple: [Isabella Van Wagenen, notableWork, “Ain’t I a Woman?” speech]
Generated description
“Ain’t I a Woman?” is the famous 1851 speech by abolitionist and women’s rights activist Sojourner Truth that powerfully challenged prevailing notions of racial and gender inferiority and inequality.

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_69e77e7d8a848190a98d0162325fd780 completed April 21, 2026, 1:41 p.m.
NER Named-entity recognition batch_69f5faa7d9f481909351f41d4beea808 completed May 2, 2026, 1:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10c14794548190b01f87937231d0e8 completed May 22, 2026, 8:49 p.m.
NEDg Description generation batch_6a10c247b5c881908c687885a5c14440 completed May 22, 2026, 8:53 p.m.
NED2 Entity disambiguation (via description) batch_6a10c2b68f648190a5b6bc8e3433af94 completed May 22, 2026, 8:55 p.m.
Created at: April 21, 2026, 6:23 p.m.