Triple

T36571724
Position Surface form Disambiguated ID Type / Status
Subject Elizabeth "Baby Doe" Tabor E902137 entity
Predicate birthName P65 FINISHED
Object Elizabeth McCourt
Elizabeth McCourt, better known as "Baby Doe" Tabor, was a famous 19th-century American socialite whose dramatic rise and fall from wealth in Colorado mining history became the subject of enduring legend and opera.
E2231487 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: Elizabeth McCourt | Statement: [Elizabeth "Baby Doe" Tabor, birthName, Elizabeth McCourt]
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: Elizabeth McCourt
Triple: [Elizabeth "Baby Doe" Tabor, birthName, Elizabeth McCourt]
Generated description
Elizabeth McCourt, better known as "Baby Doe" Tabor, was a famous 19th-century American socialite whose dramatic rise and fall from wealth in Colorado mining history became the subject of enduring legend and opera.

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_69f76e6416708190a9754b8c52d4e453 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7c2a0f01c81908e99834fba3e37c0 completed May 3, 2026, 9:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a40951606b48190ab48dbd8ecb29e2b completed June 28, 2026, 3:29 a.m.
NEDg Description generation batch_6a4096a06cd881908c727b9134edb207 completed June 28, 2026, 3:36 a.m.
NED2 Entity disambiguation (via description) batch_6a409a56d8cc81909572b61b90dba241 completed June 28, 2026, 3:51 a.m.
Created at: May 3, 2026, 4:11 p.m.