Triple

T17326195
Position Surface form Disambiguated ID Type / Status
Subject 西行 E420692 entity
Predicate 出身地 P1 FINISHED
Object 武蔵国
武蔵国は、現在のおおよそ東京都と埼玉県に相当する関東地方の古代から中世にかけての重要な令制国である。
E1263494 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: 武蔵国 | Statement: [西行, 出身地, 武蔵国]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: 武蔵国
Context triple: [西行, 出身地, 武蔵国]
  • A. Mutsu Province
    Mutsu Province was a large and historically significant region in northeastern Honshu, Japan, that played a key role in early Japanese expansion and administration.
  • B. Ibaraki
    Ibaraki is a city in northern Osaka Prefecture, Japan, known as a residential and industrial hub within the Kansai metropolitan area.
  • C. Shinano Province
    Shinano Province was an old administrative region of Japan, roughly corresponding to modern Nagano Prefecture, known for its mountainous terrain and historical significance.
  • D. Saitama Prefecture
    Saitama Prefecture is a landlocked administrative region in the Kantō area of Japan, just north of Tokyo, known for its large commuter population, industrial centers, and cultural sites.
  • E. Ibaraki Prefecture
    Ibaraki Prefecture is a region in eastern Japan known for its agriculture, coastal landscapes, and scientific research centers such as the city of Tsukuba.
  • 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: 武蔵国
Triple: [西行, 出身地, 武蔵国]
Generated description
武蔵国は、現在のおおよそ東京都と埼玉県に相当する関東地方の古代から中世にかけての重要な令制国である。
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: 武蔵国
Target entity description: 武蔵国は、現在のおおよそ東京都と埼玉県に相当する関東地方の古代から中世にかけての重要な令制国である。
  • A. Mutsu Province
    Mutsu Province was a large and historically significant region in northeastern Honshu, Japan, that played a key role in early Japanese expansion and administration.
  • B. Ibaraki
    Ibaraki is a city in northern Osaka Prefecture, Japan, known as a residential and industrial hub within the Kansai metropolitan area.
  • C. Shinano Province
    Shinano Province was an old administrative region of Japan, roughly corresponding to modern Nagano Prefecture, known for its mountainous terrain and historical significance.
  • D. Saitama Prefecture
    Saitama Prefecture is a landlocked administrative region in the Kantō area of Japan, just north of Tokyo, known for its large commuter population, industrial centers, and cultural sites.
  • E. Ibaraki Prefecture
    Ibaraki Prefecture is a region in eastern Japan known for its agriculture, coastal landscapes, and scientific research centers such as the city of Tsukuba.
  • 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_69d889d3adc881909319f1edb8d2a956 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e439d24e548190a766dd246a4d63d4 completed April 19, 2026, 2:11 a.m.
NED1 Entity disambiguation (via context triple) batch_6a018c4c2dc08190b60982abc9ac7c9c completed May 11, 2026, 7:59 a.m.
NEDg Description generation batch_6a018f2358b481908226aa84a7bd9d7f completed May 11, 2026, 8:11 a.m.
NED2 Entity disambiguation (via description) batch_6a0190125abc8190ad4d1f500e3513b3 completed May 11, 2026, 8:15 a.m.
Created at: April 10, 2026, 5:43 a.m.