Triple
T26861890
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | coat of arms of Suriname |
E676350
|
entity |
| Predicate | sinisterField |
P125985
|
FINISHED |
| Object | royal palm and diamond |
—
|
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: royal palm and diamond | Statement: [coat of arms of Suriname, sinisterField, royal palm and diamond]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sinisterField Context triple: [coat of arms of Suriname, sinisterField, royal palm and diamond]
-
A.
sinisterBaseFieldDepicts
chosen
Indicates that a base field portrays or visually represents something in a way that is associated with the sinister (left-hand) side in heraldic or symbolic terms.
-
B.
sinisterHalf
Indicates that one entity is the evil, malevolent, or dark counterpart of another entity, typically as part of a dual or split identity.
-
C.
fieldOfDeception
Indicates a context or domain in which deceptive behavior, misinformation, or misleading practices are actively employed or prevalent.
-
D.
holdsInSinisterPaw
Indicates that an entity is holding another entity in its left (sinister) paw.
-
E.
cursedIdentityOf
Indicates that one entity is the cursed or negatively transformed identity or form of another entity.
- 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_69eee9ba94bc8190b44c5d4397d04ecd |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69f61e943d40819098891ce10abd4448 |
completed | May 2, 2026, 3:56 p.m. |
| PD | Predicate disambiguation | batch_69f611ad2eb48190ac1ed0090f13f7a9 |
completed | May 2, 2026, 3:01 p.m. |
Created at: April 27, 2026, 5:25 a.m.