Triple

T20816152
Position Surface form Disambiguated ID Type / Status
Subject Hobomok E512443 entity
Predicate mainCharacter P1183 FINISHED
Object Mary Conant
Mary Conant is a central fictional figure in Lydia Maria Child’s 1824 historical novel "Hobomok," which explores themes of religious intolerance, cultural encounter, and early American identity.
E1740157 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: Mary Conant | Statement: [Hobomok, mainCharacter, Mary Conant]
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: Mary Conant
Triple: [Hobomok, mainCharacter, Mary Conant]
Generated description
Mary Conant is a central fictional figure in Lydia Maria Child’s 1824 historical novel "Hobomok," which explores themes of religious intolerance, cultural encounter, and early American identity.

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_69e0b4cd25088190b48ca9700cd24efc completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c2f3473c81908c43a2ec242b1acd completed April 21, 2026, 12:21 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1209097a1c81908674095e7a052489 completed May 23, 2026, 8:07 p.m.
NEDg Description generation batch_6a120a10905c819096fa77fad68b6bb8 completed May 23, 2026, 8:12 p.m.
NED2 Entity disambiguation (via description) batch_6a120aeed5ec819097f7ac08533bcf65 completed May 23, 2026, 8:15 p.m.
Created at: April 16, 2026, 12:41 p.m.