Triple
T34110528
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | His Majesty the King of Burma |
E874826
|
entity |
| Predicate | traditionalLanguageEquivalent |
P21030
|
FINISHED |
| Object | Burmese royal style |
—
|
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: Burmese royal style | Statement: [His Majesty the King of Burma, traditionalLanguageEquivalent, Burmese royal style]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: traditionalLanguageEquivalent Context triple: [His Majesty the King of Burma, traditionalLanguageEquivalent, Burmese royal style]
-
A.
traditionalLanguageName
chosen
Indicates the name traditionally used in a particular language to refer to the subject entity.
-
B.
languageTraditionally
Indicates that something is customarily or historically expressed, written, or communicated in a particular language.
-
C.
languageFamilyTraditional
Indicates that one entity belongs to, or is classified under, the traditional language family of the other entity.
-
D.
languageEquivalent
Indicates that two linguistic expressions convey the same meaning or function across different languages or language varieties.
-
E.
laterTraditionsLanguage
Indicates that later traditions or sources refer to or describe the subject using the specified language.
- 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_69f349a80d4481908527317d43f5c579 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69ff109695008190a22b47ef8be2e3f9 |
completed | May 9, 2026, 10:46 a.m. |
| PD | Predicate disambiguation | batch_69ff0f243ea88190970d2c520b55c816 |
completed | May 9, 2026, 10:40 a.m. |
Created at: May 1, 2026, 1:53 a.m.