Triple

T30275088
Position Surface form Disambiguated ID Type / Status
Subject Greater London House E769915 entity
Predicate formerOccupant P7727 FINISHED
Object Carreras Tobacco Company
Carreras Tobacco Company was a prominent British tobacco manufacturer best known for its Black Cat and Craven A cigarette brands and its iconic Art Deco factory in London.
E1913510 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: Carreras Tobacco Company | Statement: [Greater London House, formerOccupant, Carreras Tobacco Company]
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: Carreras Tobacco Company
Triple: [Greater London House, formerOccupant, Carreras Tobacco Company]
Generated description
Carreras Tobacco Company was a prominent British tobacco manufacturer best known for its Black Cat and Craven A cigarette brands and its iconic Art Deco factory in 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_69f224868fa8819099127eaf8855a28f completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f680d7f2588190976af8f601f9b648 completed May 2, 2026, 10:55 p.m.
NED1 Entity disambiguation (via context triple) batch_6a27892b4b4c8190bd22cfa2ac2e3e0e completed June 9, 2026, 3:31 a.m.
NEDg Description generation batch_6a2789ed38c0819091e34340435d8251 completed June 9, 2026, 3:35 a.m.
NED2 Entity disambiguation (via description) batch_6a278a76f450819095acd3e2b23d2b73 completed June 9, 2026, 3:37 a.m.
Created at: April 29, 2026, 7:44 p.m.