Triple
T12694660
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Creil |
E303298
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Nanchong |
E276212
|
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: Nanchong | Statement: [Creil, hasTwinTown, Nanchong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nanchong Context triple: [Creil, hasTwinTown, Nanchong]
-
A.
Nanchong
chosen
Nanchong is a major city in northeastern Sichuan Province, China, known as a regional transportation and economic hub with a long historical and cultural heritage.
-
B.
Deyang
Deyang is an industrial city in southwestern China known for its heavy machinery manufacturing and location near Chengdu in Sichuan Province.
-
C.
Mianyang
Mianyang is a major city in southwestern China known as an important industrial and technological center within Sichuan Province.
-
D.
Langzhong
Langzhong is an ancient county-level city in Sichuan, China, renowned for its well-preserved historic old town and traditional architecture along the Jialing River.
-
E.
Meizhou
Meizhou is a city in eastern Guangdong, China, known as a cultural and historical center of the Hakka people.
- 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_69d7bdef90d48190b46b88270e780946 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961ebd17081909f983567e4b36533 |
completed | April 10, 2026, 8:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f67c7c4c608190a1786accf8d86141 |
completed | May 2, 2026, 10:36 p.m. |
Created at: April 9, 2026, 5:22 p.m.