Triple
T84342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | House of Deputies |
E1696
|
entity |
| Predicate | typeOfRepresentation |
P103
|
FINISHED |
| Object | diocesan representation |
—
|
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: diocesan representation | Statement: [House of Deputies, typeOfRepresentation, diocesan representation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfRepresentation Context triple: [House of Deputies, typeOfRepresentation, diocesan representation]
-
A.
hasRepresentationIn
chosen
Indicates that one entity is represented, depicted, or encoded within another entity, such as a concept, object, or data structure having a corresponding representation in a specific medium or context.
-
B.
formOfRecognition
Indicates a relationship where one entity serves as an official acknowledgment, honor, or validation granted to another entity.
-
C.
typeOfInheritance
Indicates the kind or pattern of inheritance by which a trait, property, or characteristic is passed from one entity or generation to another.
-
D.
typeOfBody
Indicates that one entity is the classification or kind of physical body that the other entity is.
-
E.
codeType
Indicates the classification or category assigned to a particular code within a coding or encoding system.
- 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_69a24c8150408190910a693eb51c1f71 |
completed | Feb. 28, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69a24f4e73c081908d2da146226ef05e |
completed | Feb. 28, 2026, 2:13 a.m. |
| PD | Predicate disambiguation | batch_69a24eb469548190b38c24e81f36c838 |
completed | Feb. 28, 2026, 2:11 a.m. |
Created at: Feb. 28, 2026, 2:06 a.m.