Triple
T4798490
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tama no Higashi no Misasagi |
E106773
|
entity |
| Predicate | burialPlaceOf |
P196
|
FINISHED |
| Object |
Nagako
Nagako, better known as Empress Kōjun, was the long-serving consort of Emperor Shōwa (Hirohito) and the mother of Emperor Emeritus Akihito of Japan.
|
E471498
|
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: Nagako | Statement: [Tama no Higashi no Misasagi, burialPlaceOf, Nagako]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nagako Context triple: [Tama no Higashi no Misasagi, burialPlaceOf, Nagako]
-
A.
Yuriko
Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
-
B.
Masako
Masako is the Empress of Japan, a former diplomat and Harvard-educated member of the Imperial House known for her international background and public role.
-
C.
Shigeko
Shigeko is a Japanese feminine given name that has been borne by various notable women, including members of the imperial family.
-
D.
Tsutako
Tsutako is a Japanese given name, most notably borne by Tsutako Nakasone.
-
E.
Kazuko
Kazuko is a Japanese feminine given name commonly borne by women, including members of the imperial family.
- 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: Nagako Triple: [Tama no Higashi no Misasagi, burialPlaceOf, Nagako]
Generated description
Nagako, better known as Empress Kōjun, was the long-serving consort of Emperor Shōwa (Hirohito) and the mother of Emperor Emeritus Akihito of Japan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nagako Target entity description: Nagako, better known as Empress Kōjun, was the long-serving consort of Emperor Shōwa (Hirohito) and the mother of Emperor Emeritus Akihito of Japan.
-
A.
Yuriko
Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
-
B.
Masako
Masako is the Empress of Japan, a former diplomat and Harvard-educated member of the Imperial House known for her international background and public role.
-
C.
Shigeko
Shigeko is a Japanese feminine given name that has been borne by various notable women, including members of the imperial family.
-
D.
Tsutako
Tsutako is a Japanese given name, most notably borne by Tsutako Nakasone.
-
E.
Kazuko
Kazuko is a Japanese feminine given name commonly borne by women, including members of the imperial family.
- 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_69bd43f591c881909e5a532388b0f3f3 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6633b02c8190994d4b3543220efa |
completed | March 20, 2026, 3:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be4d9a76c08190bd19fcef378cd640 |
completed | March 21, 2026, 7:49 a.m. |
| NEDg | Description generation | batch_69be4e764b60819097aace8e7321dc0c |
completed | March 21, 2026, 7:53 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be4ef501e081908a75547e9bb52c0c |
completed | March 21, 2026, 7:55 a.m. |
Created at: March 20, 2026, 1:22 p.m.