Triple

T35270169
Position Surface form Disambiguated ID Type / Status
Subject The Green Carnation E1018641 entity
Predicate hasCharacter P2308 FINISHED
Object Lord Reggie Hastings
Lord Reggie Hastings is a fictional aristocratic dandy in Robert Hichens’ novel "The Green Carnation," satirizing the aestheticism and social circles of late Victorian London.
E2133875 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: Lord Reggie Hastings | Statement: [The Green Carnation, hasCharacter, Lord Reggie Hastings]
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: Lord Reggie Hastings
Triple: [The Green Carnation, hasCharacter, Lord Reggie Hastings]
Generated description
Lord Reggie Hastings is a fictional aristocratic dandy in Robert Hichens’ novel "The Green Carnation," satirizing the aestheticism and social circles of late Victorian London.

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_69f76de5c4788190896ad598ae7d6bc6 completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f78f9d02dc819088086c775efb9581 completed May 3, 2026, 6:10 p.m.
NED1 Entity disambiguation (via context triple) batch_6a380fbb39e48190b0f0eb91d3f675c3 completed June 21, 2026, 4:22 p.m.
NEDg Description generation batch_6a38114cb12481908b4614e2af99bd05 completed June 21, 2026, 4:29 p.m.
NED2 Entity disambiguation (via description) batch_6a381285e7dc8190ba594299a53216f4 completed June 21, 2026, 4:34 p.m.
Created at: May 3, 2026, 4:02 p.m.