Triple

T30070974
Position Surface form Disambiguated ID Type / Status
Subject Snow Wonder E764181 entity
Predicate isBasedOn P7125 FINISHED
Object Just Like the Ones We Used to Know
"Just Like the Ones We Used to Know" is a Christmas-themed short story by Connie Willis that blends humor and poignancy in a tale about an unusually heavy snowfall affecting people's lives and relationships.
 E1898316 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: Just Like the Ones We Used to Know | Statement: [Snow Wonder, isBasedOn, Just Like the Ones We Used to Know]
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: Just Like the Ones We Used to Know
Triple: [Snow Wonder, isBasedOn, Just Like the Ones We Used to Know]
Generated description
"Just Like the Ones We Used to Know" is a Christmas-themed short story by Connie Willis that blends humor and poignancy in a tale about an unusually heavy snowfall affecting people's lives and relationships.

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_69f2247221388190a13a22c47094a0ef completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f67d386a3c81909cb643a9140701ea completed May 2, 2026, 10:39 p.m.
NED1 Entity disambiguation (via context triple) batch_6a27431a309481909e7429f7515f6fc4 completed June 8, 2026, 10:32 p.m.
NEDg Description generation batch_6a2743e893708190a11e3888456906bb completed June 8, 2026, 10:36 p.m.
NED2 Entity disambiguation (via description) batch_6a274507fa3c819098819c5b1c2c4133 completed June 8, 2026, 10:41 p.m.
Created at: April 29, 2026, 7 p.m.