Triple
T8242555
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Narita |
E192571
|
entity |
| Predicate | hasJapaneseName |
P9882
|
FINISHED |
| Object |
成田市
成田市 is a city in Chiba Prefecture, Japan, best known internationally as the location of Narita International Airport, one of the Tokyo area's main gateways.
|
E721658
|
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: 成田市 | Statement: [Narita, hasJapaneseName, 成田市]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 成田市 Context triple: [Narita, hasJapaneseName, 成田市]
-
A.
川越市
川越市 is a historic city in Saitama Prefecture, Japan, famed for its well-preserved Edo-period streetscapes and traditional warehouse-style buildings that have earned it the nickname "Little Edo."
-
B.
柏原市
柏原市は、大阪府南東部に位置し、歴史ある寺社やぶどう栽培などで知られる中規模の都市です。
-
C.
高槻市
高槻市は、大阪府北部に位置し、京都と大阪の中間にあるベッドタウン兼商工業都市です。
-
D.
Ibaraki City
Ibaraki City is a suburban city in northern Osaka Prefecture, Japan, known as a residential and commercial hub between Osaka and Kyoto.
-
E.
Hadano City
Hadano City is a municipality in Kanagawa Prefecture, Japan, known for its natural scenery, including views of Mount Ōyama and surrounding mountainous 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: 成田市 Triple: [Narita, hasJapaneseName, 成田市]
Generated description
成田市 is a city in Chiba Prefecture, Japan, best known internationally as the location of Narita International Airport, one of the Tokyo area's main gateways.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 成田市 Target entity description: 成田市 is a city in Chiba Prefecture, Japan, best known internationally as the location of Narita International Airport, one of the Tokyo area's main gateways.
-
A.
川越市
川越市 is a historic city in Saitama Prefecture, Japan, famed for its well-preserved Edo-period streetscapes and traditional warehouse-style buildings that have earned it the nickname "Little Edo."
-
B.
柏原市
柏原市は、大阪府南東部に位置し、歴史ある寺社やぶどう栽培などで知られる中規模の都市です。
-
C.
高槻市
高槻市は、大阪府北部に位置し、京都と大阪の中間にあるベッドタウン兼商工業都市です。
-
D.
Ibaraki City
Ibaraki City is a suburban city in northern Osaka Prefecture, Japan, known as a residential and commercial hub between Osaka and Kyoto.
-
E.
Hadano City
Hadano City is a municipality in Kanagawa Prefecture, Japan, known for its natural scenery, including views of Mount Ōyama and surrounding mountainous 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_69ca82dc8f148190a2c75a98501a7b91 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb783f67708190a4e1c4078c3a6fb0 |
completed | March 31, 2026, 7:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd351871ac81909f8e4a72a6b99ac3 |
completed | April 1, 2026, 3:09 p.m. |
| NEDg | Description generation | batch_69cd37a5d3bc8190801b1b0f09eee462 |
completed | April 1, 2026, 3:20 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cd4edfda788190a29f5d9a7a61f6ed |
completed | April 1, 2026, 4:59 p.m. |
Created at: March 30, 2026, 5:47 p.m.