Triple
T6924537
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liao River |
E160271
|
entity |
| Predicate | associatedCity |
P3207
|
FINISHED |
| Object | Tieling |
E378881
|
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: Tieling | Statement: [Liao River, associatedCity, Tieling]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tieling Context triple: [Liao River, associatedCity, Tieling]
-
A.
Tieling
chosen
Tieling is a prefecture-level city in northeastern China known for its coal resources and location within Liaoning Province.
-
B.
Dandong
Dandong is a northeastern Chinese border city on the Yalu River, known as a key gateway for trade and transport between China and North Korea.
-
C.
Liaoyuan
Liaoyuan is a prefecture-level city in northeastern China known for its coal mining history and location in the central part of Jilin Province.
-
D.
Yingkou
Yingkou is a coastal port city in northeastern China’s Liaoning Province, known as an important industrial and shipping hub on the Bohai Sea.
-
E.
Fushun
Fushun is an industrial city in northeastern China known historically for its coal mining and heavy industry.
- 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_69c6884d350081908d8a970e4d40ad78 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6d9fea8d08190b6099a24fbac7de5 |
completed | March 27, 2026, 7:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7513bcd2c8190853bc6e8a33a1673 |
completed | March 28, 2026, 3:55 a.m. |
Created at: March 27, 2026, 2:26 p.m.