Triple

T26382627
Position Surface form Disambiguated ID Type / Status
Subject Holy Loch E663176 entity
Predicate hosted P2777 FINISHED
Object USS Canopus
USS Canopus was a U.S. Navy submarine tender that provided maintenance and support to ballistic missile submarines during the Cold War.
E1950866 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: USS Canopus | Statement: [Holy Loch, hosted, USS Canopus]
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: USS Canopus
Triple: [Holy Loch, hosted, USS Canopus]
Generated description
USS Canopus was a U.S. Navy submarine tender that provided maintenance and support to ballistic missile submarines during the Cold War.

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_69ee88374adc81909868f3bab374a32f completed April 26, 2026, 9:48 p.m.
NER Named-entity recognition batch_69f61076a94481909251f4042116768a completed May 2, 2026, 2:55 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2958dfa3388190b224e60d9fdb5aa9 completed June 10, 2026, 12:30 p.m.
NEDg Description generation batch_6a295bd1efbc8190ac520e6768c8ff69 completed June 10, 2026, 12:42 p.m.
NED2 Entity disambiguation (via description) batch_6a295c0c15648190abc3dc9308e2ed61 completed June 10, 2026, 12:43 p.m.
Created at: April 26, 2026, 11:19 p.m.