Triple

T31372883
Position Surface form Disambiguated ID Type / Status
Subject A Christmas Story: The Musical E800212 entity
Predicate settingLocation P40 FINISHED
Object Hohman, Indiana
Hohman, Indiana is a fictional Midwestern town based on author Jean Shepherd’s hometown of Hammond, Indiana, serving as the nostalgic backdrop for the story in A Christmas Story.
E1996648 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: Hohman, Indiana | Statement: [A Christmas Story: The Musical, settingLocation, Hohman, Indiana]
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: Hohman, Indiana
Triple: [A Christmas Story: The Musical, settingLocation, Hohman, Indiana]
Generated description
Hohman, Indiana is a fictional Midwestern town based on author Jean Shepherd’s hometown of Hammond, Indiana, serving as the nostalgic backdrop for the story in A Christmas Story.

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_69f224e84da08190abfc2f17494a33c8 completed April 29, 2026, 3:34 p.m.
NER Named-entity recognition batch_69f69fe9c7708190bc9488cbda8259aa completed May 3, 2026, 1:07 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2f3b613034819097cc2227c6a24cc4 completed June 14, 2026, 11:38 p.m.
NEDg Description generation batch_6a2f3c03822481908f8d5f6f99dea110 completed June 14, 2026, 11:40 p.m.
NED2 Entity disambiguation (via description) batch_6a2f3ece580081909ccc97a87984e251 completed June 14, 2026, 11:52 p.m.
Created at: April 29, 2026, 9:18 p.m.