Triple
T9345507
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yokosuka P1Y |
E224879
|
entity |
| Predicate | manufacturer |
P490
|
FINISHED |
| Object |
Nakajima
Nakajima was a major Japanese aircraft manufacturer, especially active during World War II, known for producing numerous military aircraft for the Imperial Japanese Navy and Army.
|
E792995
|
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: Nakajima | Statement: [Yokosuka P1Y, manufacturer, Nakajima]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nakajima Context triple: [Yokosuka P1Y, manufacturer, Nakajima]
-
A.
Nakajima Hikari
Nakajima Hikari is a Japanese politician who served as the successor to Nakajima Sakae 11 in public office.
-
B.
Nakajima Sakae
Nakajima Sakae is a Japanese individual whose specific public profile or notable achievements are not clearly identifiable from the available information.
-
C.
Yoshimura
Yoshimura is a Japanese surname borne by various notable individuals across fields such as politics, sports, and the arts.
-
D.
Mibuchi
Mibuchi is a Japanese surname borne by individuals such as Tadahiko Mibuchi.
-
E.
Tachikawa
Tachikawa is a major city in western Tokyo, Japan, known as a key commercial and transportation hub of the Tama region.
- 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: Nakajima Triple: [Yokosuka P1Y, manufacturer, Nakajima]
Generated description
Nakajima was a major Japanese aircraft manufacturer, especially active during World War II, known for producing numerous military aircraft for the Imperial Japanese Navy and Army.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nakajima Target entity description: Nakajima was a major Japanese aircraft manufacturer, especially active during World War II, known for producing numerous military aircraft for the Imperial Japanese Navy and Army.
-
A.
Nakajima Hikari
Nakajima Hikari is a Japanese politician who served as the successor to Nakajima Sakae 11 in public office.
-
B.
Nakajima Sakae
Nakajima Sakae is a Japanese individual whose specific public profile or notable achievements are not clearly identifiable from the available information.
-
C.
Yoshimura
Yoshimura is a Japanese surname borne by various notable individuals across fields such as politics, sports, and the arts.
-
D.
Mibuchi
Mibuchi is a Japanese surname borne by individuals such as Tadahiko Mibuchi.
-
E.
Tachikawa
Tachikawa is a major city in western Tokyo, Japan, known as a key commercial and transportation hub of the Tama region.
- 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_69ca842993248190a79ab06968994b86 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd4f0e2ccc8190a68f1c96c0886660 |
completed | April 1, 2026, 4:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0e41ccdcc8190b2939716984b2c7b |
completed | April 4, 2026, 10:12 a.m. |
| NEDg | Description generation | batch_69d0e5752a38819089b23ac52f0a6a39 |
completed | April 4, 2026, 10:18 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d0e64cb4dc81908cef7d729d9cfb4d |
completed | April 4, 2026, 10:22 a.m. |
Created at: March 30, 2026, 7:41 p.m.