Triple
T13363526
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cheung |
E318877
|
entity |
| Predicate | romanizes |
P2508
|
FINISHED |
| Object | 鄭 |
E810882
|
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: 鄭 | Statement: [Cheung, romanizes, 鄭]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 鄭 Context triple: [Cheung, romanizes, 鄭]
-
A.
鄭
chosen
鄭 is a common Korean family name of Chinese origin, typically romanized as Jeong, Jung, or Chung.
-
B.
陳
陳 is a common Chinese surname and character with historical roots, widely used across Chinese-speaking communities and often romanized as "Chan," "Chen," or similar variants.
-
C.
Zheng
Zheng is the given name of Qin Shi Huang, the first emperor who unified China and founded the Qin dynasty.
-
D.
郭
郭 is a common Chinese surname borne by numerous individuals across the Chinese-speaking world.
-
E.
Zhong Wen
Zhong Wen is the tough, determined police officer portrayed by Jackie Chan in the action film "Police Story 2013."
- 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_69d806b7bbac8190b85278c87fa7aff3 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69da628affd081909f1790d333f0eef4 |
completed | April 11, 2026, 3:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7267c99788190b158b1d9f57ceba2 |
completed | May 3, 2026, 10:42 a.m. |
Created at: April 9, 2026, 9:32 p.m.