Triple
T16846055
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kai |
E409540
|
entity |
| Predicate | hasMeaningInChinese |
P125084
|
FINISHED |
| Object | victory (depending on characters) |
—
|
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: victory (depending on characters) | Statement: [Kai, hasMeaningInChinese, victory (depending on characters)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMeaningInChinese Context triple: [Kai, hasMeaningInChinese, victory (depending on characters)]
-
A.
hasMeaningInKhmer
Indicates that something (such as a word, phrase, or symbol) possesses a specific meaning or interpretation in the Khmer language.
-
B.
hasTranslatedMeaning
Indicates that one entity expresses the meaning of another entity in a different language through translation.
-
C.
hasMeaningInSanskrit
Indicates that one entity (such as a word, phrase, or symbol) possesses a specific meaning when interpreted in the Sanskrit language.
-
D.
hasMeaningViaJohn
Indicates that something possesses or conveys its meaning specifically through John as the interpretive or mediating agent.
-
E.
hasMultipleMeanings
Indicates that a term, symbol, or expression is associated with more than one distinct meaning or interpretation.
- F. None of above. chosen
Provenance (4 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_69d883952b048190887740a980b712ed |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b3541a008190b2a97cfea92b170f |
completed | April 18, 2026, 4:37 p.m. |
| PD | Predicate disambiguation | batch_69e32b87b4248190aaddb05e88452356 |
completed | April 18, 2026, 6:58 a.m. |
| PDg | Predicate description generation | batch_69e34fb7c8c8819086975b7955b7d8ef |
completed | April 18, 2026, 9:32 a.m. |
Created at: April 10, 2026, 5:24 a.m.