Triple
T17006439
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chàhn |
E412582
|
entity |
| Predicate | originalSurnameStrokeCount |
P58361
|
FINISHED |
| Object | 16 (traditional form 陳) |
—
|
LITERAL 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: 16 (traditional form 陳) | Statement: [Chàhn, originalSurnameStrokeCount, 16 (traditional form 陳)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originalSurnameStrokeCount Context triple: [Chàhn, originalSurnameStrokeCount, 16 (traditional form 陳)]
-
A.
correspondsToChineseSurname
Indicates that one entity is the Chinese surname equivalent or counterpart of another entity.
-
B.
hasStrokeCountApprox
Indicates an approximate number of strokes associated with writing or drawing the related entity.
-
C.
hasStrokeCount
chosen
Indicates the number of strokes required to write a given symbol or character.
-
D.
nameInMcCuneReischauer
Indicates that an entity’s name is represented using the McCune–Reischauer romanization system.
-
E.
nameLengthInKanji
Indicates the number of kanji characters used in an entity’s name.
- F. None of above.
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. |
| PD | Predicate disambiguation | batch_69e35d552bc08190af17ef7659e094ef |
completed | April 18, 2026, 10:30 a.m. |
Created at: April 10, 2026, 5:32 a.m.