Triple
T10547524
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hun River |
E248860
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Fushun |
E365211
|
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: Fushun | Statement: [Hun River, flowsThrough, Fushun]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fushun Context triple: [Hun River, flowsThrough, Fushun]
-
A.
Fushun
chosen
Fushun is an industrial city in northeastern China known historically for its coal mining and heavy industry.
-
B.
Benxi
Benxi is an industrial and mining city in eastern Liaoning Province, China, known for its steel production and nearby scenic karst landscapes.
-
C.
Fuxin
Fuxin is a prefecture-level city in northeastern China known historically for its coal mining industry and location in western Liaoning Province.
-
D.
Anshan
Anshan was an ancient city and region in southwestern Iran that served as an early center of Elamite and later Achaemenid Persian power.
-
E.
Anshan
Anshan is a major industrial city in northeastern China, historically known as one of the country’s leading steel-producing centers.
- 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_69d381c733c08190ab1dd6239f5f34ae |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d526d20ef48190ab9f70d4ce5f2a11 |
completed | April 7, 2026, 3:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d94b3148dc81908720c27e58a325ab |
completed | April 10, 2026, 7:10 p.m. |
Created at: April 6, 2026, 12:33 p.m.