Triple

T37352086
Position Surface form Disambiguated ID Type / Status
Subject Mary Ritter Beard E927350 entity
Predicate notableWork P4 FINISHED
Object On Understanding Women
On Understanding Women is a pioneering feminist historical work by Mary Ritter Beard that challenges traditional narratives about women’s roles and contributions in society.
 E2223863 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: On Understanding Women | Statement: [Mary Ritter Beard, notableWork, On Understanding Women]
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: On Understanding Women
Triple: [Mary Ritter Beard, notableWork, On Understanding Women]
Generated description
On Understanding Women is a pioneering feminist historical work by Mary Ritter Beard that challenges traditional narratives about women’s roles and contributions in society.

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_69f76eb5e034819088e53ab5b7909a68 completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fb5bc124fc81909cc9146f9ebd53f4 completed May 6, 2026, 3:18 p.m.
NED1 Entity disambiguation (via context triple) batch_6a406ce0c0bc81908b91c0b10e0274a6 completed June 28, 2026, 12:37 a.m.
NEDg Description generation batch_6a406e45fdb88190a249e22b487490a3 completed June 28, 2026, 12:43 a.m.
NED2 Entity disambiguation (via description) batch_6a406ea13c788190a30eb5de2d8bccfd completed June 28, 2026, 12:45 a.m.
Created at: May 3, 2026, 4:16 p.m.