Triple

T37903754
Position Surface form Disambiguated ID Type / Status
Subject Spanish Canada E945489 entity
Predicate hadSettlement P16159 FINISHED
Object Fort San Miguel
Fort San Miguel was a Spanish colonial fortification and settlement on Vancouver Island that served as Spain’s northernmost outpost on the Pacific Northwest coast in the late 18th century.
 E2248774 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: Fort San Miguel | Statement: [Spanish Canada, hadSettlement, Fort San Miguel]
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: Fort San Miguel
Triple: [Spanish Canada, hadSettlement, Fort San Miguel]
Generated description
Fort San Miguel was a Spanish colonial fortification and settlement on Vancouver Island that served as Spain’s northernmost outpost on the Pacific Northwest coast in the late 18th 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_69f76ef20bb0819088b5b6ceecb0b8fc completed May 3, 2026, 3:51 p.m.
NER Named-entity recognition batch_69fbbd56e8d88190aa5fdc489f4ed5dd completed May 6, 2026, 10:14 p.m.
NED1 Entity disambiguation (via context triple) batch_6a410cc6d23081908cd3310a8bae5d85 completed June 28, 2026, noon
NEDg Description generation batch_6a410d36ac008190b23036ecdf4159d5 completed June 28, 2026, 12:01 p.m.
NED2 Entity disambiguation (via description) batch_6a410e50a2d0819092ce4ff0863ecbc2 completed June 28, 2026, 12:06 p.m.
Created at: May 3, 2026, 4:20 p.m.