Triple

T25099659
Position Surface form Disambiguated ID Type / Status
Subject London water supply system E628687 entity
Predicate includesReservoir P79074 FINISHED
Object Queen Elizabeth II Reservoir
The Queen Elizabeth II Reservoir is a large raw water storage reservoir in Surrey, England, serving as a key component of London's public water supply infrastructure.
E1688300 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: Queen Elizabeth II Reservoir | Statement: [London water supply system, includesReservoir, Queen Elizabeth II Reservoir]
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: Queen Elizabeth II Reservoir
Triple: [London water supply system, includesReservoir, Queen Elizabeth II Reservoir]
Generated description
The Queen Elizabeth II Reservoir is a large raw water storage reservoir in Surrey, England, serving as a key component of London's public water supply infrastructure.

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_69e2ff3071548190b62d1ac237397197 completed April 18, 2026, 3:49 a.m.
NER Named-entity recognition batch_69f464bc496081909bad8c973386eea4 completed May 1, 2026, 8:30 a.m.
NED1 Entity disambiguation (via context triple) batch_6a10b71bde4c8190b2777dadae6e67d2 completed May 22, 2026, 8:05 p.m.
NEDg Description generation batch_6a10b94377108190a5fb35e99b5f0351 completed May 22, 2026, 8:14 p.m.
NED2 Entity disambiguation (via description) batch_6a10b9c6dbf48190abe4efb4035db2a0 completed May 22, 2026, 8:17 p.m.
Created at: April 18, 2026, 6:25 a.m.