Triple
T17162530
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Citroën Xantia |
E416515
|
entity |
| Predicate | assemblyLocation |
P40
|
FINISHED |
| Object | Wuhan, China |
E1680
|
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: Wuhan, China | Statement: [Citroën Xantia, assemblyLocation, Wuhan, China]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wuhan, China Context triple: [Citroën Xantia, assemblyLocation, Wuhan, China]
-
A.
Wuhan
chosen
Wuhan is a major city in central China, known as a key industrial, commercial, and transportation hub located at the confluence of the Yangtze and Han rivers.
-
B.
Hsiangcheng, China
Hsiangcheng, China is a town in Henan Province known as the birthplace of author and social critic Os Guinness.
-
C.
Port of Wuhan
The Port of Wuhan is a major inland river port on the Yangtze River in central China, serving as a key hub for regional trade and transportation.
-
D.
central Wuhan
Central Wuhan is the bustling commercial and cultural heart of Wuhan, known for its dense urban development, major shopping streets, and historic riverfront areas.
-
E.
Shenzhen, China
Shenzhen, China is a major southern Chinese metropolis known for its rapid transformation into a global technology and manufacturing hub bordering Hong Kong.
- 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_69d886d279c081909f8ff1f743ddeb69 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3f91316108190b0d856d6fa5cd509 |
completed | April 18, 2026, 9:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a015fc687788190864fa3922a31184d |
completed | May 11, 2026, 4:49 a.m. |
Created at: April 10, 2026, 5:37 a.m.