Triple

T37374378
Position Surface form Disambiguated ID Type / Status
Subject Little Big Man E927933 entity
Predicate frameNarrator P17575 FINISHED
Object Ralph Fielding Snell
Ralph Fielding Snell is the fictional historian and editor who frames the narrative in Thomas Berger’s novel "Little Big Man" by presenting and commenting on Jack Crabb’s purported memoirs.
E2240354 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: Ralph Fielding Snell | Statement: [Little Big Man, frameNarrator, Ralph Fielding Snell]
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: Ralph Fielding Snell
Triple: [Little Big Man, frameNarrator, Ralph Fielding Snell]
Generated description
Ralph Fielding Snell is the fictional historian and editor who frames the narrative in Thomas Berger’s novel "Little Big Man" by presenting and commenting on Jack Crabb’s purported memoirs.

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_69f76eb820248190a5c395ca50ad002a completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fb8d103fe881908966c684f0415986 completed May 6, 2026, 6:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a40d661b1b08190a69d92fe18144c4f completed June 28, 2026, 8:08 a.m.
NEDg Description generation batch_6a40d7bf02748190a814ea213256baa8 completed June 28, 2026, 8:13 a.m.
NED2 Entity disambiguation (via description) batch_6a40d96828bc819080bc6fa16564db9f completed June 28, 2026, 8:20 a.m.
Created at: May 3, 2026, 4:16 p.m.