Triple
T7946639
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zhuanxu |
E184514
|
entity |
| Predicate | alternativeName |
P39
|
FINISHED |
| Object | Gaoyang Shi |
E704511
|
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: Gaoyang Shi | Statement: [Zhuanxu, alternativeName, Gaoyang Shi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gaoyang Shi Context triple: [Zhuanxu, alternativeName, Gaoyang Shi]
-
A.
Gaoyang
chosen
Gaoyang is a legendary figure in ancient Chinese mythology, often associated with early royal lineages and revered as an ancestral progenitor by various clans.
-
B.
Zhaoyuan
Zhaoyuan is a county-level city in eastern China's Shandong province, known for its rich gold mining industry and economic development.
-
C.
Zunhua City
Zunhua City is a county-level city in northeastern Hebei Province, China, known for its historical sites and administrative affiliation with the prefecture-level city of Tangshan.
-
D.
Bozhou
Bozhou is a historic city in northern Anhui Province, China, known as a major center of traditional Chinese medicine and ancient culture.
-
E.
Leiyang City
Leiyang City is a county-level city administered by Hengyang in Hunan Province, China, known for its long history and role as a regional industrial and transportation hub.
- 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_69ca8291c2008190b1b8832c87814bcf |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3b29a570819091a2ac185a8d57c4 |
completed | March 31, 2026, 3:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc5650cfb08190846e040f85c8369d |
completed | March 31, 2026, 11:18 p.m. |
Created at: March 30, 2026, 5:09 p.m.