Triple
T5599194
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jilin Province |
E147072
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
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.
|
E546298
|
NE FINISHED |
How this triple was built (4 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: Liaoyuan | Statement: [Jilin Province, containsCity, Liaoyuan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Liaoyuan Context triple: [Jilin Province, containsCity, Liaoyuan]
-
A.
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.
-
B.
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.
-
C.
Anshan
Anshan is a major industrial city in northeastern China, historically known as one of the country’s leading steel-producing centers.
-
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.
Benxi
Benxi is an industrial and mining city in eastern Liaoning Province, China, known for its steel production and nearby scenic karst landscapes.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Liaoyuan Triple: [Jilin Province, containsCity, Liaoyuan]
Generated description
Liaoyuan is a prefecture-level city in northeastern China known for its coal mining history and location in the central part of Jilin Province.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Liaoyuan Target entity description: Liaoyuan is a prefecture-level city in northeastern China known for its coal mining history and location in the central part of Jilin Province.
-
A.
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.
-
B.
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.
-
C.
Anshan
Anshan was an ancient city and region in southwestern Iran that served as an early center of Elamite and later Achaemenid Persian power.
-
D.
Anshan
Anshan is a major industrial city in northeastern China, historically known as one of the country’s leading steel-producing centers.
-
E.
Benxi
Benxi is an industrial and mining city in eastern Liaoning Province, China, known for its steel production and nearby scenic karst landscapes.
- F. None of above. chosen
Provenance (5 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_69c009043d648190a7af89698ccf1e3e |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c020d82870819087f9591b5a1021ce |
completed | March 22, 2026, 5:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c097c85fa481909dc6dcfccce8efa8 |
completed | March 23, 2026, 1:30 a.m. |
| NEDg | Description generation | batch_69c09882e3188190a24199e5bcc7e76f |
completed | March 23, 2026, 1:33 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c0991cdc9c81908ef92c3dbfe4276a |
completed | March 23, 2026, 1:36 a.m. |
Created at: March 22, 2026, 3:38 p.m.