Triple
T3644447
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | China National Highway 106 |
E77263
|
entity |
| Predicate | passesThroughCity |
P416
|
FINISHED |
| Object | Xinxiang |
E214214
|
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: Xinxiang | Statement: [China National Highway 106, passesThroughCity, Xinxiang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Xinxiang Context triple: [China National Highway 106, passesThroughCity, Xinxiang]
-
A.
Xinxiang
chosen
Xinxiang is a prefecture-level industrial and transportation hub city located in northern Henan Province, China.
-
B.
Xuchang
Xuchang is a historically significant city in central China, known as a former capital during the Three Kingdoms period and now an important industrial and transportation hub.
-
C.
Zhoukou
Zhoukou is a prefecture-level city in eastern Henan Province, China, known as an important agricultural and transportation hub with historical and cultural significance.
-
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.
Liuyang
Liuyang is a county-level city in Hunan Province, China, known for its fireworks industry and cultural heritage.
- 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_69ad85de1b988190a45f8dbfebc806fc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc35c28908190b253f4835918a2b4 |
completed | March 8, 2026, 6:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b48836f5d08190bbf0b6410ed6f766 |
completed | March 13, 2026, 9:57 p.m. |
Created at: March 8, 2026, 3:24 p.m.