Triple
T13299507
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Central Tai |
E316769
|
entity |
| Predicate | hasLanguage |
P15
|
FINISHED |
| Object |
Lianshan Zhuang
Lianshan Zhuang is a Tai language spoken by the Zhuang people in the Lianshan area of Guangdong, China.
|
E1033825
|
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: Lianshan Zhuang | Statement: [Central Tai, hasLanguage, Lianshan Zhuang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lianshan Zhuang Context triple: [Central Tai, hasLanguage, Lianshan Zhuang]
-
A.
Liangjiazhuang
Liangjiazhuang is a town in Shanxi Province, China, that serves as the administrative center of Wutai County.
-
B.
Xinzhuang
Xinzhuang is a major suburban town and transportation hub in Shanghai, China, known for its busy commercial areas and key metro and rail connections.
-
C.
Junlian County
Junlian County is a county-level administrative region under the jurisdiction of Yibin City in Sichuan Province, China, known for its mountainous terrain and agricultural economy.
-
D.
Zhushan County
Zhushan County is a mountainous county-level division in northwestern Hubei Province, China, administered by the prefecture-level city of Shiyan.
-
E.
Huangcun
Huangcun is a town in Beijing, China, that serves as the administrative and commercial center of the city's southern Daxing District.
- 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: Lianshan Zhuang Triple: [Central Tai, hasLanguage, Lianshan Zhuang]
Generated description
Lianshan Zhuang is a Tai language spoken by the Zhuang people in the Lianshan area of Guangdong, China.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lianshan Zhuang Target entity description: Lianshan Zhuang is a Tai language spoken by the Zhuang people in the Lianshan area of Guangdong, China.
-
A.
Liangjiazhuang
Liangjiazhuang is a town in Shanxi Province, China, that serves as the administrative center of Wutai County.
-
B.
Xinzhuang
Xinzhuang is a major suburban town and transportation hub in Shanghai, China, known for its busy commercial areas and key metro and rail connections.
-
C.
Junlian County
Junlian County is a county-level administrative region under the jurisdiction of Yibin City in Sichuan Province, China, known for its mountainous terrain and agricultural economy.
-
D.
Zhushan County
Zhushan County is a mountainous county-level division in northwestern Hubei Province, China, administered by the prefecture-level city of Shiyan.
-
E.
Huangcun
Huangcun is a town in Beijing, China, that serves as the administrative and commercial center of the city's southern Daxing District.
- 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_69d806b40ab4819094adf6c374f4811a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d990a43ed88190a8dbbbd7d6d62dc4 |
completed | April 11, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f716dd0cd88190b0ae81b402fc31cf |
completed | May 3, 2026, 9:35 a.m. |
| NEDg | Description generation | batch_69f71842a8808190ae4ef8b22bdbd0c6 |
completed | May 3, 2026, 9:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f71908236481909140a5953ec44498 |
completed | May 3, 2026, 9:44 a.m. |
Created at: April 9, 2026, 9:28 p.m.