Triple
T7606906
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toyooka |
E180128
|
entity |
| Predicate | mergedWith |
P77
|
FINISHED |
| Object |
Takeno
Takeno was a former town in Hyōgo Prefecture, Japan, that became part of the expanded city of Toyooka following a municipal merger.
|
E691394
|
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: Takeno | Statement: [Toyooka, mergedWith, Takeno]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Takeno Context triple: [Toyooka, mergedWith, Takeno]
-
A.
Takanami
Takanami was a Japanese destroyer of the Imperial Japanese Navy during World War II, notable for being sunk in the Battle of Tassafaronga in 1942.
-
B.
Takaishi
Takaishi is a city in Osaka Prefecture, Japan, known as a small industrial and residential hub within the Osaka metropolitan area.
-
C.
Shimotsuki
Shimotsuki was a Japanese destroyer of the Imperial Japanese Navy that served in World War II before being sunk in late 1944.
-
D.
Takayoshi
Takayoshi is a Japanese given name notably borne by Kido Takayoshi, a key samurai and statesman of the Meiji Restoration.
-
E.
Saito
Saito is a Japanese surname commonly borne by notable figures in fields such as politics, sports, and the arts.
- 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: Takeno Triple: [Toyooka, mergedWith, Takeno]
Generated description
Takeno was a former town in Hyōgo Prefecture, Japan, that became part of the expanded city of Toyooka following a municipal merger.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Takeno Target entity description: Takeno was a former town in Hyōgo Prefecture, Japan, that became part of the expanded city of Toyooka following a municipal merger.
-
A.
Takanami
Takanami was a Japanese destroyer of the Imperial Japanese Navy during World War II, notable for being sunk in the Battle of Tassafaronga in 1942.
-
B.
Takaishi
Takaishi is a city in Osaka Prefecture, Japan, known as a small industrial and residential hub within the Osaka metropolitan area.
-
C.
Shimotsuki
Shimotsuki was a Japanese destroyer of the Imperial Japanese Navy that served in World War II before being sunk in late 1944.
-
D.
Takayoshi
Takayoshi is a Japanese given name notably borne by Kido Takayoshi, a key samurai and statesman of the Meiji Restoration.
-
E.
Saito
Saito is a Japanese surname commonly borne by notable figures in fields such as politics, sports, and the arts.
- 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_69c69f3567008190ab01d2ca7b53584a |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f9fe10408190b1c12bb8f911cea8 |
completed | March 27, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c97ca3d5e0819097e904184202b75f |
completed | March 29, 2026, 7:25 p.m. |
| NEDg | Description generation | batch_69c9811249bc8190ace23dd28bb8e38a |
completed | March 29, 2026, 7:44 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c98196bdd0819097d886d961924f51 |
completed | March 29, 2026, 7:46 p.m. |
Created at: March 27, 2026, 3:54 p.m.