Triple
T14168565
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 伊藤 清 |
E351143
|
entity |
| Predicate | 出身地 |
P1
|
FINISHED |
| Object |
千葉県
千葉県は、日本の関東地方に位置し、成田国際空港や東京湾アクアライン、房総半島の海岸などで知られる県です。
|
E1168962
|
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: 千葉県 | Statement: [伊藤 清, 出身地, 千葉県]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 千葉県 Context triple: [伊藤 清, 出身地, 千葉県]
-
A.
Ibaraki Prefecture
Ibaraki Prefecture is a region in eastern Japan known for its agriculture, coastal landscapes, and scientific research centers such as the city of Tsukuba.
-
B.
Ibaraki
Ibaraki is a city in northern Osaka Prefecture, Japan, known as a residential and industrial hub within the Kansai metropolitan area.
-
C.
Kanagawa Prefecture
Kanagawa Prefecture is a coastal region in Japan’s Kantō area, known for its major port city of Yokohama, historic Kamakura, and proximity to Tokyo.
-
D.
Saitama Prefecture
Saitama Prefecture is a landlocked administrative region in the Kantō area of Japan, just north of Tokyo, known for its large commuter population, industrial centers, and cultural sites.
-
E.
Miyagi Prefecture
Miyagi Prefecture is a coastal region in Japan’s Tōhoku area, known for its capital city Sendai, scenic Matsushima Bay, and a mix of urban centers and rich natural landscapes.
- 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: 千葉県 Triple: [伊藤 清, 出身地, 千葉県]
Generated description
千葉県は、日本の関東地方に位置し、成田国際空港や東京湾アクアライン、房総半島の海岸などで知られる県です。
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 千葉県 Target entity description: 千葉県は、日本の関東地方に位置し、成田国際空港や東京湾アクアライン、房総半島の海岸などで知られる県です。
-
A.
Ibaraki Prefecture
Ibaraki Prefecture is a region in eastern Japan known for its agriculture, coastal landscapes, and scientific research centers such as the city of Tsukuba.
-
B.
Ibaraki
Ibaraki is a city in northern Osaka Prefecture, Japan, known as a residential and industrial hub within the Kansai metropolitan area.
-
C.
Kanagawa Prefecture
Kanagawa Prefecture is a coastal region in Japan’s Kantō area, known for its major port city of Yokohama, historic Kamakura, and proximity to Tokyo.
-
D.
Saitama Prefecture
Saitama Prefecture is a landlocked administrative region in the Kantō area of Japan, just north of Tokyo, known for its large commuter population, industrial centers, and cultural sites.
-
E.
Miyagi Prefecture
Miyagi Prefecture is a coastal region in Japan’s Tōhoku area, known for its capital city Sendai, scenic Matsushima Bay, and a mix of urban centers and rich natural landscapes.
- 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_69d8278775fc8190b0802d22ca2f495d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61b355f08190864c7322bbcb766d |
completed | April 14, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff677935408190a28af4cd34d82aa4 |
completed | May 9, 2026, 4:57 p.m. |
| NEDg | Description generation | batch_69ff67f64d2c81908fd2d8a09cd0b369 |
completed | May 9, 2026, 4:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff6888a85481909e8cdd34ed230fa4 |
completed | May 9, 2026, 5:02 p.m. |
Created at: April 10, 2026, 1 a.m.