Triple
T3291399
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Handan |
E69108
|
entity |
| Predicate | hasCountyLevelCity |
P27799
|
FINISHED |
| Object |
Wu’an
Wu’an is a county-level city administered by Handan in Hebei Province, northern China, known for its industrial development and coal resources.
|
E345074
|
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: Wu’an | Statement: [Handan, hasCountyLevelCity, Wu’an]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wu’an Context triple: [Handan, hasCountyLevelCity, Wu’an]
-
A.
Yuncheng
Yuncheng is a major city in southern Shanxi Province, China, known for its historical sites and role as a regional transportation and economic hub.
-
B.
Bozhou
Bozhou is a historic city in northern Anhui Province, China, known as a major center of traditional Chinese medicine and ancient culture.
-
C.
Changzhi
Changzhi is a major city in southeastern Shanxi Province, China, known as a regional industrial and transportation hub with a long historical and cultural heritage.
-
D.
Lu'an
Lu'an is a prefecture-level city in western Anhui Province, China, known for its mountainous terrain and tea production.
-
E.
Lüliang
Lüliang is a prefecture-level city in western Shanxi Province, China, known for its mountainous terrain and significant coal and energy resources.
- 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: Wu’an Triple: [Handan, hasCountyLevelCity, Wu’an]
Generated description
Wu’an is a county-level city administered by Handan in Hebei Province, northern China, known for its industrial development and coal resources.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Wu’an Target entity description: Wu’an is a county-level city administered by Handan in Hebei Province, northern China, known for its industrial development and coal resources.
-
A.
Yuncheng
Yuncheng is a major city in southern Shanxi Province, China, known for its historical sites and role as a regional transportation and economic hub.
-
B.
Bozhou
Bozhou is a historic city in northern Anhui Province, China, known as a major center of traditional Chinese medicine and ancient culture.
-
C.
Changzhi
Changzhi is a major city in southeastern Shanxi Province, China, known as a regional industrial and transportation hub with a long historical and cultural heritage.
-
D.
Lu'an
Lu'an is a prefecture-level city in western Anhui Province, China, known for its mountainous terrain and tea production.
-
E.
Lüliang
Lüliang is a prefecture-level city in western Shanxi Province, China, known for its mountainous terrain and significant coal and energy resources.
- 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_69ad859d45748190b0742408c954b39f |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb05d0c908190a927d1af78e27de0 |
completed | March 8, 2026, 5:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2e8654e8481908f4a8efa219edc54 |
completed | March 12, 2026, 4:23 p.m. |
| NEDg | Description generation | batch_69b2ec67e79c8190b29970f856c7e5cf |
completed | March 12, 2026, 4:40 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b2ed1b17a88190ac06092ad012bd9b |
completed | March 12, 2026, 4:43 p.m. |
Created at: March 8, 2026, 3:10 p.m.