Triple

T31040344
Position Surface form Disambiguated ID Type / Status
Subject Alfred Ludlow E790971 entity
Predicate parent P120 FINISHED
Object Colonel William Ludlow
Colonel William Ludlow is a fictional patriarch and retired U.S. Army officer from the film and novella "Legends of the Fall," known for his complex relationship with his three sons in early 20th-century Montana.
E779210 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: Colonel William Ludlow | Statement: [Alfred Ludlow, parent, Colonel William Ludlow]
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: Colonel William Ludlow
Triple: [Alfred Ludlow, parent, Colonel William Ludlow]
Generated description
Colonel William Ludlow is a fictional patriarch and retired U.S. Army officer from the film and novella "Legends of the Fall," known for his complex relationship with his three sons in early 20th-century Montana.

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_69f224ca2fa881908a3ac5fedf207b90 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f694f996f081909a1498d6da15358f completed May 3, 2026, 12:21 a.m.
NED1 Entity disambiguation (via context triple) batch_6a292b0a9a4481908a68d64530a27e69 completed June 10, 2026, 9:14 a.m.
NEDg Description generation batch_6a292c2ac4c88190b9f13431e329828c completed June 10, 2026, 9:19 a.m.
NED2 Entity disambiguation (via description) batch_6a292cc4074c8190ad11b0dde89b515f completed June 10, 2026, 9:22 a.m.
Created at: April 29, 2026, 8:59 p.m.