Triple
T2312064
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peng Chun Chang |
E51980
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Peng |
E51981
|
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: Peng | Statement: [Peng Chun Chang, familyName, Peng]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peng Context triple: [Peng Chun Chang, familyName, Peng]
-
A.
Peng
chosen
Peng is a Chinese surname borne by numerous notable figures in politics, arts, and academia throughout Chinese history and the modern era.
-
B.
Penge
Penge is a suburban district in southeast London known for its Victorian architecture and proximity to Crystal Palace.
-
C.
Poh Pitu
Poh Pitu was an early capital city of the Medang Kingdom, an ancient Javanese Hindu-Buddhist polity in what is now Indonesia.
-
D.
Pekar
Pekar is a surname of Central or Eastern European origin, notably associated with several cultural and artistic figures.
-
E.
Wu
Wu is a common Chinese surname borne by many notable individuals across politics, academia, entertainment, and sports.
- 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_69a88b0bb30c81908ded03b006d29387 |
completed | March 4, 2026, 7:42 p.m. |
| NER | Named-entity recognition | batch_69abc61a8e248190b5024cca9efd806d |
completed | March 7, 2026, 6:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae895c95388190848f592fc5d48ec6 |
completed | March 9, 2026, 8:48 a.m. |
Created at: March 4, 2026, 7:49 p.m.