Triple
T17006407
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chʻên |
E412581
|
entity |
| Predicate | correspondsToPinyin |
P41219
|
FINISHED |
| Object | Chén |
E113104
|
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: Chén | Statement: [Chʻên, correspondsToPinyin, Chén]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chén Context triple: [Chʻên, correspondsToPinyin, Chén]
-
A.
Chen
chosen
Chen is a common Chinese surname borne by many notable individuals across politics, arts, science, and technology.
-
B.
Chou
Chou is the comic or clown role type in traditional Chinese Peking opera, known for its humorous, witty, and often satirical performances.
-
C.
Chou
Chou is an alternative romanization of the Chinese surname and dynasty name commonly spelled "Zhou" in pinyin.
-
D.
Chou
Chou is a romanized spelling commonly used to represent the Japanese name "Chō" in English and other Latin-alphabet contexts.
-
E.
Bchamoun
Bchamoun is a suburban town in Lebanon known for its strategic hilltop location overlooking Beirut and its role as a residential and commercial hub in the Mount Lebanon region.
- 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_69d886cb581c8190ab05f4b429c9cd85 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d3831268819089286053a5acf653 |
completed | April 18, 2026, 6:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a011b451ff88190b63f4ebddf93f153 |
completed | May 10, 2026, 11:56 p.m. |
Created at: April 10, 2026, 5:32 a.m.