Triple

T18475890
Position Surface form Disambiguated ID Type / Status
Subject Morihiro Konoe E451430 entity
Predicate memberOf P10 FINISHED
Object kazoku
Kazoku was the hereditary peerage system of Japan’s nobility, established during the Meiji era and modeled partly on European aristocratic classes.
 E1326807 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: kazoku | Statement: [Morihiro Konoe, memberOf, kazoku]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: kazoku
Context triple: [Morihiro Konoe, memberOf, kazoku]
  • A. kazasker
    A kazasker was a high-ranking Ottoman military judge responsible for overseeing legal matters and the judiciary, particularly in the empire’s army and provinces.
  • B. KAZO
    KAZO is the ICAO airport code for Kalamazoo/Battle Creek International Airport in Michigan, United States.
  • C. KAZ
    KAZ is the three-letter ISO 3166-1 alpha-3 country code assigned to Kazakhstan for international standardization and identification.
  • D. kazy
    Kazy is a traditional Kazakh sausage made from horse meat and fat, typically seasoned and air-dried, and served as a festive delicacy.
  • E. Kaz
    Kaz is a central protagonist in the Disney XD series "Mighty Med," known as a comic book fan who becomes a sidekick and caretaker to real-life superheroes.
  • 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: kazoku
Triple: [Morihiro Konoe, memberOf, kazoku]
Generated description
Kazoku was the hereditary peerage system of Japan’s nobility, established during the Meiji era and modeled partly on European aristocratic classes.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: kazoku
Target entity description: Kazoku was the hereditary peerage system of Japan’s nobility, established during the Meiji era and modeled partly on European aristocratic classes.
  • A. kazasker
    A kazasker was a high-ranking Ottoman military judge responsible for overseeing legal matters and the judiciary, particularly in the empire’s army and provinces.
  • B. KAZO
    KAZO is the ICAO airport code for Kalamazoo/Battle Creek International Airport in Michigan, United States.
  • C. KAZ
    KAZ is the three-letter ISO 3166-1 alpha-3 country code assigned to Kazakhstan for international standardization and identification.
  • D. kazy
    Kazy is a traditional Kazakh sausage made from horse meat and fat, typically seasoned and air-dried, and served as a festive delicacy.
  • E. Kaz
    Kaz is a central protagonist in the Disney XD series "Mighty Med," known as a comic book fan who becomes a sidekick and caretaker to real-life superheroes.
  • 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_69d8d38465a0819099b9b42d2a662ac1 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e53062f67881909620c4e8fc00eb7d completed April 19, 2026, 7:43 p.m.
NED1 Entity disambiguation (via context triple) batch_6a043f2cada88190991e2e7b0d55de58 completed May 13, 2026, 9:06 a.m.
NEDg Description generation batch_6a0440531f6c8190a080d372d063fb33 completed May 13, 2026, 9:11 a.m.
NED2 Entity disambiguation (via description) batch_6a0441a395688190a376ae5bfe99a30f completed May 13, 2026, 9:17 a.m.
Created at: April 10, 2026, 11:34 a.m.