Triple
T5855076
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | statue of William Lyon Mackenzie King |
E130130
|
entity |
| Predicate | hasLanguageOnPlaque |
P67509
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [statue of William Lyon Mackenzie King, hasLanguageOnPlaque, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageOnPlaque Context triple: [statue of William Lyon Mackenzie King, hasLanguageOnPlaque, English]
-
A.
hasNumberOfNamesInscribed
Indicates the quantity of distinct names that are inscribed on a given entity.
-
B.
hasGraveInscription
Indicates that an entity (typically a grave or tomb) bears a specific inscription engraved or written on it.
-
C.
hasCommemorativePlaques
Indicates that one entity possesses or displays commemorative plaques associated with it.
-
D.
materialTypicallyInscribedOn
Indicates the material that is most commonly used as the surface or medium on which something is inscribed.
-
E.
languageOfHonoredFigure
Indicates the language associated with or used by the person who is being honored.
- 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_69c0084de39081909eb34e6bed74215a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c044ab0a048190b84be40fb13c0f50 |
completed | March 22, 2026, 7:36 p.m. |
| PD | Predicate disambiguation | batch_69c03345ca0c819081c81148d054fed2 |
completed | March 22, 2026, 6:21 p.m. |
| PDg | Predicate description generation | batch_69c044a9c4f0819081b8c196932883f6 |
completed | March 22, 2026, 7:36 p.m. |
Created at: March 22, 2026, 3:55 p.m.