Triple

T18601695
Position Surface form Disambiguated ID Type / Status
Subject Fort Ross E454634 entity
Predicate foundedBy P104 FINISHED
Object Ivan Kuskov
Ivan Kuskov was a Russian explorer and colonial administrator best known for leading the Russian-American Company’s expansion into California and establishing the settlement of Fort Ross in the early 19th century.
E2147551 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: Ivan Kuskov | Statement: [Fort Ross, foundedBy, Ivan Kuskov]
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: Ivan Kuskov
Triple: [Fort Ross, foundedBy, Ivan Kuskov]
Generated description
Ivan Kuskov was a Russian explorer and colonial administrator best known for leading the Russian-American Company’s expansion into California and establishing the settlement of Fort Ross in the early 19th century.

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_69d8d38ae7e081908a98df1251842402 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e5475112608190acacc5ac7a08c4a0 completed April 19, 2026, 9:21 p.m.
NED1 Entity disambiguation (via context triple) batch_6a385bb22d0c8190a2fbb1d8f570d805 completed June 21, 2026, 9:46 p.m.
NEDg Description generation batch_6a385cfee66c8190a546393089b8d789 completed June 21, 2026, 9:51 p.m.
NED2 Entity disambiguation (via description) batch_6a385df5220881908ae1a6c6e999e3fa completed June 21, 2026, 9:56 p.m.
Created at: April 10, 2026, 11:45 a.m.