Triple

T28340641
Position Surface form Disambiguated ID Type / Status
Subject Sophia Alekseyevna E717799 entity
Predicate supportedBy P67 FINISHED
Object Streltsy regiments
The Streltsy regiments were elite but increasingly politicized units of the Russian tsarist infantry, known for their role in palace coups and power struggles in the late 17th century.
 E1814569 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: Streltsy regiments | Statement: [Sophia Alekseyevna, supportedBy, Streltsy regiments]
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: Streltsy regiments
Triple: [Sophia Alekseyevna, supportedBy, Streltsy regiments]
Generated description
The Streltsy regiments were elite but increasingly politicized units of the Russian tsarist infantry, known for their role in palace coups and power struggles in the late 17th 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_69eff6eb30388190b898b96c4be6f49d completed April 27, 2026, 11:53 p.m.
NER Named-entity recognition batch_69f64bd805f08190a6b503bf965ebbf5 completed May 2, 2026, 7:09 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1627bec9ac819094444c5b5130ceb8 completed May 26, 2026, 11:07 p.m.
NEDg Description generation batch_6a162a68838481908eed21663497c78a completed May 26, 2026, 11:19 p.m.
NED2 Entity disambiguation (via description) batch_6a162af3b7308190851819d6989be119 completed May 26, 2026, 11:21 p.m.
Created at: April 28, 2026, 12:39 a.m.