Triple
T11152404
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shogun |
E263817
|
entity |
| Predicate | leadCharacter |
P1668
|
FINISHED |
| Object |
Lady Mariko
Lady Mariko is a noblewoman and key political and romantic figure in James Clavell’s historical novel "Shōgun," set in feudal Japan.
|
E908418
|
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: Lady Mariko | Statement: [Shogun, leadCharacter, Lady Mariko]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lady Mariko Context triple: [Shogun, leadCharacter, Lady Mariko]
-
A.
Ruroo
Ruroo is a Japanese illustrator best known for providing the character artwork for the light novel series "Oreshura."
-
B.
Marika Tachibana
Marika Tachibana is a main heroine in the romantic comedy anime and manga series "Nisekoi," known as Raku Ichijou’s assertive and devoted arranged fiancée.
-
C.
Yuriko
Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
-
D.
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.
-
E.
Marisa
Marisa is a feminine given name of Latin origin, commonly used in Spanish- and Italian-speaking cultures.
- 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: Lady Mariko Triple: [Shogun, leadCharacter, Lady Mariko]
Generated description
Lady Mariko is a noblewoman and key political and romantic figure in James Clavell’s historical novel "Shōgun," set in feudal Japan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lady Mariko Target entity description: Lady Mariko is a noblewoman and key political and romantic figure in James Clavell’s historical novel "Shōgun," set in feudal Japan.
-
A.
Ruroo
Ruroo is a Japanese illustrator best known for providing the character artwork for the light novel series "Oreshura."
-
B.
Marika Tachibana
Marika Tachibana is a main heroine in the romantic comedy anime and manga series "Nisekoi," known as Raku Ichijou’s assertive and devoted arranged fiancée.
-
C.
Yuriko
Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
-
D.
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.
-
E.
Marisa
Marisa is a feminine given name of Latin origin, commonly used in Spanish- and Italian-speaking cultures.
- 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_69d6aa9ccddc8190868998c8b7beb060 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8719e74819095413abc6c79296c |
completed | April 9, 2026, 5:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e46334125c81909e2807b2c7e573b9 |
completed | April 19, 2026, 5:08 a.m. |
| NEDg | Description generation | batch_69e46c3448348190b2c062d21771066d |
completed | April 19, 2026, 5:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e46dadbc5c8190b41279a05731dc95 |
completed | April 19, 2026, 5:52 a.m. |
Created at: April 8, 2026, 9:28 p.m.