Triple
T13992344
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beijing–Kowloon Railway |
E336610
|
entity |
| Predicate | connectsCity |
P4245
|
FINISHED |
| Object | Xinyang |
E293862
|
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: Xinyang | Statement: [Beijing–Kowloon Railway, connectsCity, Xinyang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Xinyang Context triple: [Beijing–Kowloon Railway, connectsCity, Xinyang]
-
A.
Xinyang
chosen
Xinyang is a prefecture-level city in southeastern Henan Province, China, known for its tea production and location near the Dabie Mountains.
-
B.
Liuyang
Liuyang is a county-level city in Hunan Province, China, known for its fireworks industry and cultural heritage.
-
C.
Feicheng
Feicheng is a county-level city in Shandong Province, China, administered by the prefecture-level city of Tai'an.
-
D.
Shangqiu
Shangqiu is a historic prefecture-level city in eastern Henan Province, China, known as one of the country’s ancient capitals and an important regional transportation hub.
-
E.
Longyan
Longyan is a prefecture-level city in western Fujian Province, China, known for its Hakka culture, mountainous landscapes, and historic tulou earthen dwellings.
- 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_69d81c639e808190a0e4b4f3d31c6a59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2eb3b5d881909f15a1e08bb202f3 |
completed | April 14, 2026, 12:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcb652305c81908ea097d4f36a05c0 |
completed | May 7, 2026, 3:57 p.m. |
Created at: April 9, 2026, 10:19 p.m.