Triple

T34508343
Position Surface form Disambiguated ID Type / Status
Subject Ephraim McDowell E885948 entity
Predicate performedOn P270 FINISHED
Object Jane Todd Crawford
Jane Todd Crawford was an early 19th-century American woman historically noted as the patient in one of the first successful abdominal surgeries, a pioneering ovariotomy performed without anesthesia.
E2102772 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: Jane Todd Crawford | Statement: [Ephraim McDowell, performedOn, Jane Todd Crawford]
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: Jane Todd Crawford
Triple: [Ephraim McDowell, performedOn, Jane Todd Crawford]
Generated description
Jane Todd Crawford was an early 19th-century American woman historically noted as the patient in one of the first successful abdominal surgeries, a pioneering ovariotomy performed without anesthesia.

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_69f349cc0220819081f154c6964f4dc2 completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f71f5a467881909ee6095dfd9b87bc completed May 3, 2026, 10:11 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3736160eb4819091d21826f898332b completed June 21, 2026, 12:53 a.m.
NEDg Description generation batch_6a37372aa9608190a607c9b4d0c4f978 completed June 21, 2026, 12:58 a.m.
NED2 Entity disambiguation (via description) batch_6a373a7631588190a8fb371e7e7338ac completed June 21, 2026, 1:12 a.m.
Created at: May 1, 2026, 2:01 a.m.