Triple

T1139212
Position Surface form Disambiguated ID Type / Status
Subject Grand Duke Michael Alexandrovich of Russia E23409 entity
Predicate associatedWithPlace P2830 FINISHED
Object Perm
Perm is a major industrial and cultural city in the Ural region of Russia, situated on the Kama River and historically significant as a gateway between European and Asian Russia.
 E129564 NE FINISHED

How this triple was built (4 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: Perm | Statement: [Grand Duke Michael Alexandrovich of Russia, associatedWithPlace, Perm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Perm
Context triple: [Grand Duke Michael Alexandrovich of Russia, associatedWithPlace, Perm]
  • A. Per
    Per is a Scandinavian masculine given name, commonly used in Norway, Sweden, and Denmark as a form of Peter.
  • B. Pol
    Pol is a given name and variant of Paul, used in several European languages such as Catalan and French.
  • C. Pe
    Pe is a Hebrew consonant letter that represents a "p" or "f" sound and has both standard and final written forms.
  • D. PM
    PM is the international vehicle registration code assigned to the French overseas collectivity of Saint Pierre and Miquelon.
  • E. PM
    PM is the abbreviation for the U.S. Department of State’s Bureau of Political-Military Affairs, which manages security assistance, defense trade, and political-military relations.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Perm
Triple: [Grand Duke Michael Alexandrovich of Russia, associatedWithPlace, Perm]
Generated description
Perm is a major industrial and cultural city in the Ural region of Russia, situated on the Kama River and historically significant as a gateway between European and Asian Russia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Perm
Target entity description: Perm is a major industrial and cultural city in the Ural region of Russia, situated on the Kama River and historically significant as a gateway between European and Asian Russia.
  • A. Per
    Per is a Scandinavian masculine given name, commonly used in Norway, Sweden, and Denmark as a form of Peter.
  • B. Pol
    Pol is a given name and variant of Paul, used in several European languages such as Catalan and French.
  • C. Pe
    Pe is a Hebrew consonant letter that represents a "p" or "f" sound and has both standard and final written forms.
  • D. PM
    PM is the international vehicle registration code assigned to the French overseas collectivity of Saint Pierre and Miquelon.
  • E. PM
    PM is the abbreviation for the U.S. Department of State’s Bureau of Political-Military Affairs, which manages security assistance, defense trade, and political-military relations.
  • F. None of above. chosen

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_69a493ef399c8190b04b9146d2314f59 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4bc27c88881909c64ec30b7f66575 completed March 1, 2026, 10:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac59b020d48190bc6ecbdb720c6779 completed March 7, 2026, 5 p.m.
NEDg Description generation batch_69ac5a7599048190a46b0d560270ffa4 completed March 7, 2026, 5:03 p.m.
NED2 Entity disambiguation (via description) batch_69ac5af24a948190a37c832508149a48 completed March 7, 2026, 5:05 p.m.
Created at: March 1, 2026, 7:44 p.m.