Triple
T37666147
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cap Kingdom |
E937822
|
entity |
| Predicate | has2DSection |
P188521
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Cap Kingdom, has2DSection, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: has2DSection Context triple: [Cap Kingdom, has2DSection, yes]
-
A.
hasVerticalSection
Indicates that one entity possesses or includes a distinct vertical section or segment as part of its structure or representation.
-
B.
hasSectionAlong
Indicates that one entity includes or runs along a specific segment or portion of another entity.
-
C.
isMultiSectional
Indicates that something is composed of or divided into multiple distinct sections or parts.
-
D.
hasSect
Indicates that an entity includes, contains, or is associated with a particular sect or subgroup within a larger religious, ideological, or organizational context.
-
E.
hasSurfaceSections
Indicates that an entity is composed of or divided into distinct sections or parts of its surface.
- 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_69f76ed6df7c8190b018e5baea716ceb |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fbaa19e4a88190b04f26c0d4e708fd |
completed | May 6, 2026, 8:52 p.m. |
| PD | Predicate disambiguation | batch_69fba887821c8190ae93ef1dd389e9c8 |
completed | May 6, 2026, 8:45 p.m. |
| PDg | Predicate description generation | batch_69fba9ddef488190b785d332580650f0 |
completed | May 6, 2026, 8:51 p.m. |
Created at: May 3, 2026, 4:18 p.m.