Triple
T164586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | God Save the King |
E2984
|
entity |
| Predicate | typicalOpeningLine |
P829
|
FINISHED |
| Object | God save our gracious King |
—
|
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: God save our gracious King | Statement: [God Save the King, typicalOpeningLine, God save our gracious King]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalOpeningLine Context triple: [God Save the King, typicalOpeningLine, God save our gracious King]
-
A.
openingLine
chosen
Indicates that one entity is the first line or initial statement that begins another entity, such as a text, speech, or conversation.
-
B.
typicalGreeting
Indicates the standard or commonly used way one entity greets another in a given context.
-
C.
typicalSpeaker
Indicates that the subject is a prototypical or characteristic speaker or source of utterances in the context of the object.
-
D.
openingTheme
Indicates that one entity serves as the opening theme (such as a song or musical piece) for another entity, typically a show, series, or similar work.
-
E.
typicalBlendPartner
Indicates that two entities are commonly or characteristically combined or mixed together as standard or usual partners.
- 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_69a2524ce1e48190ab066bf72859f474 |
completed | Feb. 28, 2026, 2:26 a.m. |
| NER | Named-entity recognition | batch_69a258827da481909b20ea5e9d21676f |
completed | Feb. 28, 2026, 2:52 a.m. |
| PD | Predicate disambiguation | batch_69a2566392208190a538ea9aa1fac53e |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:34 a.m.