Triple
T13375000
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coat of arms of Kenya |
E319160
|
entity |
| Predicate | lionSymbolism |
P41568
|
FINISHED |
| Object | protection |
—
|
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: protection | Statement: [Coat of arms of Kenya, lionSymbolism, protection]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lionSymbolism Context triple: [Coat of arms of Kenya, lionSymbolism, protection]
-
A.
animalSymbol
chosen
Indicates that one entity serves as a symbolic representation or emblem of the other in the form of an animal.
-
B.
shapeSymbolism
Indicates how a particular shape is associated with or conveys symbolic meaning within a given context.
-
C.
lionAttribute
Indicates that one entity has an attribute, property, or characteristic related to a lion in relation to another entity.
-
D.
symbolismIn
Indicates that one entity functions as a symbol or representation within the context, meaning, or interpretive framework of another entity.
-
E.
lionSupporterRepresents
Indicates that an entity serves as a representative or advocate for a lion supporter, acting on their behalf or symbolizing their interests.
- 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_69d806b886bc8190b676e7768b8e01c5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dadcda64a48190b53243a763cd175b |
completed | April 11, 2026, 11:44 p.m. |
| PD | Predicate disambiguation | batch_69d9a02c9abc8190b328e7bae747bfc5 |
completed | April 11, 2026, 1:13 a.m. |
Created at: April 9, 2026, 9:33 p.m.