Triple
T12494372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Port Arthur |
E298645
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | Lüshunkou |
E267905
|
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: Lüshunkou | Statement: [Port Arthur, alsoKnownAs, Lüshunkou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lüshunkou Context triple: [Port Arthur, alsoKnownAs, Lüshunkou]
-
A.
Lüshunkou
chosen
Lüshunkou is a strategically important port city at the tip of the Liaodong Peninsula in northeastern China, historically known as Port Arthur and the site of major naval and military conflicts.
-
B.
Wudaokou
Wudaokou is a bustling neighborhood in Beijing known for its universities, tech companies, and vibrant student nightlife.
-
C.
Laohekou City
Laohekou City is a county-level city in northwestern Hubei Province, China, known as a regional transport and commercial hub under the administration of Xiangyang.
-
D.
Lianyungang
Lianyungang is a major coastal city and seaport in eastern China, serving as an important transportation and trade hub on the Yellow Sea.
-
E.
Wafangdian
Wafangdian is a county-level city in Liaoning Province, China, known for its bearing industry and as an important satellite city of Dalian.
- 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_69d6ada377208190a36011199a4d8558 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94de4089c8190917a45365e641437 |
completed | April 10, 2026, 7:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6556e9180819084ddb984754b0b54 |
completed | May 2, 2026, 7:50 p.m. |
Created at: April 8, 2026, 9:56 p.m.