Triple

T26359083
Position Surface form Disambiguated ID Type / Status
Subject Mr. Blandings Builds His Dream House E660149 entity
Predicate basedOnAuthor P2806 FINISHED
Object Eric Hodgins
Eric Hodgins was an American author best known for his humorous novel "Mr. Blandings Builds His Dream House," which satirizes the trials of building a home.
E1731470 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: Eric Hodgins | Statement: [Mr. Blandings Builds His Dream House, basedOnAuthor, Eric Hodgins]
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: Eric Hodgins
Triple: [Mr. Blandings Builds His Dream House, basedOnAuthor, Eric Hodgins]
Generated description
Eric Hodgins was an American author best known for his humorous novel "Mr. Blandings Builds His Dream House," which satirizes the trials of building a home.

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_69ee8126d52c8190bc0b34337c2c9aa8 completed April 26, 2026, 9:18 p.m.
NER Named-entity recognition batch_69f60ff21bc08190994f406feb484aad completed May 2, 2026, 2:53 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11c7fa19f081908de3613d0990f7e2 completed May 23, 2026, 3:30 p.m.
NEDg Description generation batch_6a11c97a0b8c8190930222a24b8ef5be completed May 23, 2026, 3:36 p.m.
NED2 Entity disambiguation (via description) batch_6a11ca61b1408190ab4bda33e53cb27c completed May 23, 2026, 3:40 p.m.
Created at: April 26, 2026, 10:50 p.m.