Triple
T5066114
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shenyang Railway Station |
E114147
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object | Anshan |
E362130
|
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: Anshan | Statement: [Shenyang Railway Station, nearbyCity, Anshan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anshan Context triple: [Shenyang Railway Station, nearbyCity, Anshan]
-
A.
Anshan
Anshan was an ancient city and region in southwestern Iran that served as an early center of Elamite and later Achaemenid Persian power.
-
B.
Anshan
chosen
Anshan is a major industrial city in northeastern China, historically known as one of the country’s leading steel-producing centers.
-
C.
Benxi
Benxi is an industrial and mining city in eastern Liaoning Province, China, known for its steel production and nearby scenic karst landscapes.
-
D.
Fushun
Fushun is an industrial city in northeastern China known historically for its coal mining and heavy industry.
-
E.
Jinzhou
Jinzhou is a prefecture-level port city in southwestern Liaoning Province, northeastern China, known for its industrial base and coastal location on the Bohai Sea.
- 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_69bd443c0c8c81908663b77afb28e165 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd749aceac8190817278266308fd64 |
completed | March 20, 2026, 4:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bfb70320ac819088ba3b1da868d9a4 |
completed | March 22, 2026, 9:31 a.m. |
Created at: March 20, 2026, 1:38 p.m.