Triple
T2929837
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nagano |
E78933
|
entity |
| Predicate | hasSisterCity |
P919
|
FINISHED |
| Object | Shijiazhuang, China |
E79577
|
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: Shijiazhuang, China | Statement: [Nagano, hasSisterCity, Shijiazhuang, China]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shijiazhuang, China Context triple: [Nagano, hasSisterCity, Shijiazhuang, China]
-
A.
Shijiazhuang
chosen
Shijiazhuang is the capital and largest city of Hebei Province in northern China, known as a major industrial and transportation hub.
-
B.
Cangzhou
Cangzhou is a prefecture-level city in eastern Hebei Province, China, known for its location near the Bohai Sea and its traditional martial arts heritage.
-
C.
Baoding
Baoding is a historic prefecture-level city in central Hebei Province, China, known as a regional transportation hub and former military and administrative center.
-
D.
Hengshui
Hengshui is a prefecture-level city in southeastern Hebei Province, China, known for its traditional culture, agriculture, and growing industrial base.
-
E.
Langfang
Langfang is a prefecture-level city in northern China situated between Beijing and Tianjin, known for its strategic location and growing industrial and service sectors.
- 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_69ad8b0d40b481908bc2a5fa2e73c3fb |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad98002da4819098d6448eebcafad4 |
completed | March 8, 2026, 3:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b086703868819083eacc3fe392fde1 |
completed | March 10, 2026, 9 p.m. |
Created at: March 8, 2026, 2:55 p.m.