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.