Triple
T32934024
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seri Setia Mahkota |
E842477
|
entity |
| Predicate | hasMalayMeaning |
P141363
|
FINISHED |
| Object | Most Faithful to the Crown |
—
|
NE NERFINISHED |
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: Most Faithful to the Crown | Statement: [Seri Setia Mahkota, hasMalayMeaning, Most Faithful to the Crown]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMalayMeaning Context triple: [Seri Setia Mahkota, hasMalayMeaning, Most Faithful to the Crown]
-
A.
hasMalayName
Indicates that an entity is associated with a specific name expressed in the Malay language.
-
B.
meansInMalay
chosen
Indicates that one entity is the meaning or translation of another entity in the Malay language.
-
C.
hasMeaningInVietnamese
Indicates that something (such as a word, phrase, or symbol) possesses a specific meaning when used in the Vietnamese language.
-
D.
hasMeaningInChinese
Indicates that one entity (such as a word, phrase, or symbol) possesses a specific meaning or interpretation within the Chinese language.
-
E.
hasTranslatedMeaning
Indicates that one entity expresses the meaning of another entity in a different language through translation.
- 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_69f34948adfc8190a937f1f622783c0b |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a00b3d2e0c88190a8bf49576e654f64 |
completed | May 10, 2026, 4:35 p.m. |
| PD | Predicate disambiguation | batch_6a00b376f4e48190a6676779fe02ea9f |
completed | May 10, 2026, 4:33 p.m. |
Created at: May 1, 2026, 1:20 a.m.