Triple

T6719798
Position Surface form Disambiguated ID Type / Status
Subject Worcester Foregate Street railway station E153365 entity
Predicate streetAddress P606 FINISHED
Object Foregate Street
Foregate Street is a central thoroughfare in Worcester, England, known for its railway station and proximity to the city’s main commercial and historic areas.
 E2294821 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: Foregate Street | Statement: [Worcester Foregate Street railway station, streetAddress, Foregate 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: Foregate Street
Triple: [Worcester Foregate Street railway station, streetAddress, Foregate Street]
Generated description
Foregate Street is a central thoroughfare in Worcester, England, known for its railway station and proximity to the city’s main commercial and historic areas.

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_69c68809b4608190a2509ddb5ab87f05 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d137084881908e04ee6b2bd45585 completed March 27, 2026, 6:49 p.m.
NED1 Entity disambiguation (via context triple) batch_6a7c217078b48190a9f30fd3ace2b6b4 completed Aug. 12, 2026, 7:32 a.m.
NEDg Description generation batch_6a7c21b59b988190a731dfae135e7264 completed Aug. 12, 2026, 7:33 a.m.
NED2 Entity disambiguation (via description) batch_6a7c2226566c81908dd9098338f8f1b9 completed Aug. 12, 2026, 7:35 a.m.
Created at: March 27, 2026, 2:07 p.m.