Triple
T18014685
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Later Yan |
E430970
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Longcheng |
—
|
NE NERFINISHED |
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: Longcheng | Statement: [Later Yan, capital, Longcheng]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Longcheng Context triple: [Later Yan, capital, Longcheng]
-
A.
Longcheng
chosen
Longcheng was the principal royal city and political center of the Xiongnu confederation in ancient Inner Asia.
-
B.
Chengguan
Chengguan was an influential Tang dynasty Buddhist monk and scholar renowned for his authoritative commentaries on Huayan (Avatamsaka) doctrine.
-
C.
Luòyáng
Luòyáng is an ancient Chinese city in Henan Province that served as the capital for multiple dynasties and is renowned as one of the cradles of Chinese civilization.
-
D.
Shangyuan
Shangyuan was a Chinese imperial era name used during the reign of Emperor Suzong of the Tang dynasty.
-
E.
Guangdu
Guangdu is an ancient historical name for the area now known as Nanchong, a major city in Sichuan Province, China.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b522e84c8190a03f6445df9f5ac8 |
completed | April 19, 2026, 10:57 a.m. |
Created at: April 10, 2026, 10:24 a.m.