Triple
T6343472
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lausanne Cathedral |
E142687
|
entity |
| Predicate | numberOfOrgans |
P70090
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [Lausanne Cathedral, numberOfOrgans, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfOrgans Context triple: [Lausanne Cathedral, numberOfOrgans, 1]
-
A.
hasOrganSystem
Indicates that an entity possesses or is associated with a particular organ system as part of its biological structure or function.
-
B.
mainOrganOf
Indicates that one entity is the primary or central organ responsible for the core functions of another entity (such as an organism or system).
-
C.
имеетОрган
Indicates that one entity possesses or contains a specific organ as a part of its body.
-
D.
organLocation
Indicates the anatomical location or position of an organ within a body or organism.
-
E.
organType
Indicates that one entity is classified as a specific type or category of organ in relation to another entity.
- 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_69c008d5ab108190b346c465696824a9 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c06745b3d88190bcabc2bf5d75555d |
completed | March 22, 2026, 10:03 p.m. |
| PD | Predicate disambiguation | batch_69c060ea1a988190889e47b7e0c819b8 |
completed | March 22, 2026, 9:36 p.m. |
| PDg | Predicate description generation | batch_69c0623bb29081908bfdfb84a07ece90 |
completed | March 22, 2026, 9:42 p.m. |
Created at: March 22, 2026, 4:31 p.m.