Triple
T7825895
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | In a Grove |
E181243
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Masago
Masago is the wife of the murdered samurai in Ryūnosuke Akutagawa’s short story "In a Grove," whose conflicting testimony is central to the tale’s exploration of truth and perspective.
|
E695271
|
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: Masago | Statement: [In a Grove, mainCharacter, Masago]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Masago Context triple: [In a Grove, mainCharacter, Masago]
-
A.
Ebi
Ebi is a short form of the German given name Eberhard, typically used as an affectionate nickname.
-
B.
Nori
Nori is a given name, often used as a short or affectionate form of longer names such as Nora.
-
C.
Oshiage
Oshiage is a district in Sumida, Tokyo, best known as the location of the Tokyo Skytree and its surrounding commercial complex.
-
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.
Shiso
Shiso is a small inland city in Japan’s Hyogo Prefecture known for its mountainous scenery, forests, and outdoor recreation.
- 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: Masago Triple: [In a Grove, mainCharacter, Masago]
Generated description
Masago is the wife of the murdered samurai in Ryūnosuke Akutagawa’s short story "In a Grove," whose conflicting testimony is central to the tale’s exploration of truth and perspective.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Masago Target entity description: Masago is the wife of the murdered samurai in Ryūnosuke Akutagawa’s short story "In a Grove," whose conflicting testimony is central to the tale’s exploration of truth and perspective.
-
A.
Ebi
Ebi is a short form of the German given name Eberhard, typically used as an affectionate nickname.
-
B.
Nori
Nori is a given name, often used as a short or affectionate form of longer names such as Nora.
-
C.
Oshiage
Oshiage is a district in Sumida, Tokyo, best known as the location of the Tokyo Skytree and its surrounding commercial complex.
-
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.
Shiso
Shiso is a small inland city in Japan’s Hyogo Prefecture known for its mountainous scenery, forests, and outdoor recreation.
- 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_69ca8282ccec819083c48efb72d21cf9 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb04a6185481908462079bd2827642 |
completed | March 30, 2026, 11:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb14aefd4881908ffa5825f4ba6eff |
completed | March 31, 2026, 12:26 a.m. |
| NEDg | Description generation | batch_69cb1734e5d88190a3d894199ee2fbfe |
completed | March 31, 2026, 12:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cb1a6fc16c8190827593f58b9d742d |
completed | March 31, 2026, 12:50 a.m. |
Created at: March 30, 2026, 4:43 p.m.