Triple

T27842927
Position Surface form Disambiguated ID Type / Status
Subject Mount Marathon Race E703735 entity
Predicate locatedOn P40 FINISHED
Object Mount Marathon
Mount Marathon is a steep, rugged mountain near Seward, Alaska, best known for hosting the annual Mount Marathon Race, one of the oldest and most challenging mountain races in the United States.
E1808265 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: Mount Marathon | Statement: [Mount Marathon Race, locatedOn, Mount Marathon]
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: Mount Marathon
Triple: [Mount Marathon Race, locatedOn, Mount Marathon]
Generated description
Mount Marathon is a steep, rugged mountain near Seward, Alaska, best known for hosting the annual Mount Marathon Race, one of the oldest and most challenging mountain races in the United States.

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_69ef840d9e3c819093615ebff4ec22be completed April 27, 2026, 3:43 p.m.
NER Named-entity recognition batch_69f638d75ba48190bfd2a602f4c5361a completed May 2, 2026, 5:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a15e68c1ee0819087523902ffa6822c completed May 26, 2026, 6:29 p.m.
NEDg Description generation batch_6a15e7ebbe3c8190886a959072625fa0 completed May 26, 2026, 6:35 p.m.
NED2 Entity disambiguation (via description) batch_6a15ed5b346c8190888ef61373cee561 completed May 26, 2026, 6:58 p.m.
Created at: April 27, 2026, 6:04 p.m.