Triple

T19746173
Position Surface form Disambiguated ID Type / Status
Subject Swansea Grand Theatre E474256 entity
Predicate streetAddress P606 FINISHED
Object Singleton Street
Singleton Street is a central thoroughfare in Swansea, Wales, known for housing the Swansea Grand Theatre and other key city-centre amenities.
 E1880617 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: Singleton Street | Statement: [Swansea Grand Theatre, streetAddress, Singleton Street]
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: Singleton Street
Triple: [Swansea Grand Theatre, streetAddress, Singleton Street]
Generated description
Singleton Street is a central thoroughfare in Swansea, Wales, known for housing the Swansea Grand Theatre and other key city-centre amenities.

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_69d8e51940a0819087bd2996f98da668 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e6529387ac819094e4d66d630e8b98 completed April 20, 2026, 4:21 p.m.
NED1 Entity disambiguation (via context triple) batch_6a26aa4387b48190827f5e9c3557c8a7 completed June 8, 2026, 11:40 a.m.
NEDg Description generation batch_6a26ae71575081908f792ba4e0bce3f2 completed June 8, 2026, 11:58 a.m.
NED2 Entity disambiguation (via description) batch_6a26b2549840819082678037e8c97eb2 completed June 8, 2026, 12:15 p.m.
Created at: April 10, 2026, 1:47 p.m.