Triple

T33477594
Position Surface form Disambiguated ID Type / Status
Subject Rawdon Crawley E857368 entity
Predicate alsoKnownAs P39 FINISHED
Object Colonel Rawdon Crawley
Colonel Rawdon Crawley is a fictional British army officer and gambler from William Makepeace Thackeray’s novel "Vanity Fair," known for his marriage to Becky Sharp and his financial imprudence.
E2052935 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 Rawdon Crawley | Statement: [Rawdon Crawley, alsoKnownAs, Colonel Rawdon Crawley]
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 Rawdon Crawley
Triple: [Rawdon Crawley, alsoKnownAs, Colonel Rawdon Crawley]
Generated description
Colonel Rawdon Crawley is a fictional British army officer and gambler from William Makepeace Thackeray’s novel "Vanity Fair," known for his marriage to Becky Sharp and his financial imprudence.

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_69f3497472508190b300ebd3fd402367 completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6e52a6d40819084472f6072c91e9f completed May 3, 2026, 6:03 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3595afe0308190be64dfe36ecf061d completed June 19, 2026, 7:17 p.m.
NEDg Description generation batch_6a35969aeed08190a8b76d38e1d471f1 completed June 19, 2026, 7:20 p.m.
NED2 Entity disambiguation (via description) batch_6a35974196b08190a4b7b8769b842cda completed June 19, 2026, 7:23 p.m.
Created at: May 1, 2026, 1:38 a.m.