Triple

T26546519
Position Surface form Disambiguated ID Type / Status
Subject The Landmark London E671547 entity
Predicate hasRestaurant P4442 FINISHED
Object Winter Garden Restaurant
Winter Garden Restaurant is an elegant, glass-roofed dining venue located in the atrium of The Landmark London hotel, known for its refined British and European cuisine and afternoon tea.
 E1730955 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: Winter Garden Restaurant | Statement: [The Landmark London, hasRestaurant, Winter Garden Restaurant]
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: Winter Garden Restaurant
Triple: [The Landmark London, hasRestaurant, Winter Garden Restaurant]
Generated description
Winter Garden Restaurant is an elegant, glass-roofed dining venue located in the atrium of The Landmark London hotel, known for its refined British and European cuisine and afternoon tea.

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_69eeb32163f08190af5f81282738e27a completed April 27, 2026, 12:51 a.m.
NER Named-entity recognition batch_69f6143625e08190a39cf2de7d6ed033 completed May 2, 2026, 3:11 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11c819b32c8190aaca1f8db245732e completed May 23, 2026, 3:30 p.m.
NEDg Description generation batch_6a11c966cc288190804da81d474872dd completed May 23, 2026, 3:36 p.m.
NED2 Entity disambiguation (via description) batch_6a11ca7256dc81908499e290c0b32b39 completed May 23, 2026, 3:40 p.m.
Created at: April 27, 2026, 1:44 a.m.