Triple
T13494428
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Naha urban area |
E320722
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Nanjo
Nanjo is a coastal city in southern Okinawa, Japan, known for its scenic landscapes, historic sites, and role as part of the greater Naha metropolitan area.
|
E1301152
|
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: Nanjo | Statement: [Naha urban area, hasPart, Nanjo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nanjo Context triple: [Naha urban area, hasPart, Nanjo]
-
A.
Miyakonojō
Miyakonojō is a city in Miyazaki Prefecture on Japan’s Kyushu island, known for its agriculture and livestock production.
-
B.
Miyoshi
Miyoshi is a Japanese city known for its scenic river valleys, historical sites, and cultural exchanges with its international sister cities.
-
C.
Kahama
Kahama is a town and district-level administrative center in northwestern Tanzania known for its mining activities and role as a commercial hub in the Shinyanga area.
-
D.
Takashima
Takashima is a lakeside city in western Shiga Prefecture, Japan, known for its scenic location along Lake Biwa and surrounding mountains.
-
E.
Takashima
Takashima is a prominent commercial and waterfront district in Nishi-ku, Yokohama, known for major shopping complexes and modern urban development.
- 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: Nanjo Triple: [Naha urban area, hasPart, Nanjo]
Generated description
Nanjo is a coastal city in southern Okinawa, Japan, known for its scenic landscapes, historic sites, and role as part of the greater Naha metropolitan area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nanjo Target entity description: Nanjo is a coastal city in southern Okinawa, Japan, known for its scenic landscapes, historic sites, and role as part of the greater Naha metropolitan area.
-
A.
Miyakonojō
Miyakonojō is a city in Miyazaki Prefecture on Japan’s Kyushu island, known for its agriculture and livestock production.
-
B.
Miyoshi
Miyoshi is a Japanese city known for its scenic river valleys, historical sites, and cultural exchanges with its international sister cities.
-
C.
Kahama
Kahama is a town and district-level administrative center in northwestern Tanzania known for its mining activities and role as a commercial hub in the Shinyanga area.
-
D.
Takashima
Takashima is a lakeside city in western Shiga Prefecture, Japan, known for its scenic location along Lake Biwa and surrounding mountains.
-
E.
Takashima
Takashima is a prominent commercial and waterfront district in Nishi-ku, Yokohama, known for major shopping complexes and modern urban development.
- 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_69d807629d6c8190998f1b9bb12d2ed0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaf4da2c88190a867b53529d39545 |
completed | April 12, 2026, 2:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a034307ff088190874d6c00f2741400 |
completed | May 12, 2026, 3:11 p.m. |
| NEDg | Description generation | batch_6a03445913088190b8662601da93a3ab |
completed | May 12, 2026, 3:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a034517f4148190864d916f4d1ef5ba |
completed | May 12, 2026, 3:19 p.m. |
Created at: April 9, 2026, 9:43 p.m.