Triple

T17369612
Position Surface form Disambiguated ID Type / Status
Subject Emperor Antoku E422275 entity
Predicate posthumousName P744 FINISHED
Object Antoku NE ONDG

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: Antoku | Statement: [Emperor Antoku, posthumousName, Antoku]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Antoku
Context triple: [Emperor Antoku, posthumousName, Antoku]
  • A. Ōtoku
    Ōtoku was a Japanese era name (nengō) of the late 11th century, used during the reign of Emperor Shirakawa.
  • B. Kutama
    Kutama is a rural village in the Zvimba District of northern Zimbabwe, known primarily as the birthplace of former president Robert Mugabe.
  • C. Okoto
    Okoto is one of Bulgaria’s glacial Seven Rila Lakes, known for its deep, eye-shaped basin set high in the Rila Mountains.
  • D. Tocho
    Tocho is the common nickname for the Tokyo Metropolitan Government Building, a prominent skyscraper complex in Shinjuku that houses Tokyo’s metropolitan administration and offers popular observation decks.
  • E. Takanot
    Takanot are rabbinic enactments or decrees established to address communal needs and clarify or safeguard Jewish law within Rabbinic Judaism.
  • 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: Antoku
Triple: [Emperor Antoku, posthumousName, Antoku]
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Antoku
Target entity description: Antoku was a child emperor of Japan from the late Heian period, remembered for his tragic death during the naval Battle of Dan-no-ura in 1185, which marked the fall of the Taira clan.
  • A. Ōtoku
    Ōtoku was a Japanese era name (nengō) of the late 11th century, used during the reign of Emperor Shirakawa.
  • B. Kutama
    Kutama is a rural village in the Zvimba District of northern Zimbabwe, known primarily as the birthplace of former president Robert Mugabe.
  • C. Okoto
    Okoto is one of Bulgaria’s glacial Seven Rila Lakes, known for its deep, eye-shaped basin set high in the Rila Mountains.
  • D. Tocho
    Tocho is the common nickname for the Tokyo Metropolitan Government Building, a prominent skyscraper complex in Shinjuku that houses Tokyo’s metropolitan administration and offers popular observation decks.
  • E. Takanot
    Takanot are rabbinic enactments or decrees established to address communal needs and clarify or safeguard Jewish law within Rabbinic Judaism.
  • F. None of above. chosen

Provenance (4 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_69d889d6535c81908be333c01deaec4e completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e43a6842388190940235198fa50041 completed April 19, 2026, 2:14 a.m.
NED1 Entity disambiguation (via context triple) batch_6a019566da6c819083b59e0911d02bd5 completed May 11, 2026, 8:37 a.m.
NEDg Description generation batch_6a01962ae4848190b2aad8e19bf6522f in_progress May 11, 2026, 8:41 a.m.
Created at: April 10, 2026, 5:44 a.m.