Triple
T11313646
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lüshunkou |
E267905
|
entity |
| Predicate | formerName |
P65
|
FINISHED |
| Object |
Ryojun
Ryojun is the former Japanese name for Lüshunkou, a strategically important port city in northeastern China historically known for its military significance.
|
E926912
|
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: Ryojun | Statement: [Lüshunkou, formerName, Ryojun]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ryojun Context triple: [Lüshunkou, formerName, Ryojun]
-
A.
Ryūō
Ryūō is a town in Shiga Prefecture, Japan, known for its location near Lake Biwa and its blend of rural landscapes with growing commercial development.
-
B.
Kenjirō
Kenjirō is a Japanese masculine given name that can be written with various kanji combinations and is borne by multiple notable individuals in fields such as sports, arts, and entertainment.
-
C.
Yorihito
Yorihito was a Japanese imperial prince of the Higashifushimi-no-miya house who served as a high-ranking naval officer during the late Meiji and Taishō periods.
-
D.
Shinpei
Shinpei is a Japanese given name commonly used for males and borne by various notable figures in politics, arts, and entertainment.
-
E.
Naoyoshi
Naoyoshi is a Japanese given name commonly used for males.
- 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: Ryojun Triple: [Lüshunkou, formerName, Ryojun]
Generated description
Ryojun is the former Japanese name for Lüshunkou, a strategically important port city in northeastern China historically known for its military significance.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ryojun Target entity description: Ryojun is the former Japanese name for Lüshunkou, a strategically important port city in northeastern China historically known for its military significance.
-
A.
Ryūō
Ryūō is a town in Shiga Prefecture, Japan, known for its location near Lake Biwa and its blend of rural landscapes with growing commercial development.
-
B.
Kenjirō
Kenjirō is a Japanese masculine given name that can be written with various kanji combinations and is borne by multiple notable individuals in fields such as sports, arts, and entertainment.
-
C.
Yorihito
Yorihito was a Japanese imperial prince of the Higashifushimi-no-miya house who served as a high-ranking naval officer during the late Meiji and Taishō periods.
-
D.
Shinpei
Shinpei is a Japanese given name commonly used for males and borne by various notable figures in politics, arts, and entertainment.
-
E.
Naoyoshi
Naoyoshi is a Japanese given name commonly used for males.
- 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_69d6aaca5c24819083db46a30d86cb34 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e9c2c7b081909af8acebc8aa93aa |
completed | April 9, 2026, 6:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5e8b19b1c8190bc9147a9fc73e35b |
completed | April 20, 2026, 8:49 a.m. |
| NEDg | Description generation | batch_69e5f1557e9c8190b53ce391793b2c7f |
completed | April 20, 2026, 9:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e5f863bf7c81908969ed0a5b99f032 |
completed | April 20, 2026, 9:56 a.m. |
Created at: April 8, 2026, 9:32 p.m.