Triple
T11313647
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lüshunkou |
E267905
|
entity |
| Predicate | formerName |
P65
|
FINISHED |
| Object | Lüshun |
E301374
|
NE FINISHED |
How this triple was built (2 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: Lüshun | Statement: [Lüshunkou, formerName, Lüshun]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lüshun Context triple: [Lüshunkou, formerName, Lüshun]
-
A.
Lüshun
chosen
Lüshun is a strategically important port city in northeastern China, historically known as Port Arthur and noted for its role in several major conflicts.
-
B.
Zhizhong
Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
-
C.
Luzhi
Luzhi is an ancient canal town near Suzhou in China, renowned for its well-preserved waterways, stone bridges, and traditional Jiangnan architecture.
-
D.
Tongling
Tongling is a prefecture-level city in eastern China known for its rich copper resources and mining industry.
-
E.
Licheng
Licheng is the courtesy name of the Daoguang Emperor, a Qing dynasty ruler of China in the early 19th century.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69e50a910a2c8190b8afd4c988e64141 |
completed | April 19, 2026, 5:02 p.m. |
Created at: April 8, 2026, 9:32 p.m.