Triple
T16174504
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cité de Carcassonne |
E392526
|
entity |
| Predicate | hasNumberOfEnceintes |
P122002
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Cité de Carcassonne, hasNumberOfEnceintes, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfEnceintes Context triple: [Cité de Carcassonne, hasNumberOfEnceintes, 2]
-
A.
hasNumberOfEntrances
Indicates the relationship that specifies how many entrances an entity possesses.
-
B.
numberOfChambers
Indicates the count of distinct chambers or compartments associated with an entity.
-
C.
numberOfVestibules
Indicates the quantity of vestibules associated with or present in an entity.
-
D.
hasNumberOfMainRooms
Indicates the relationship that specifies how many main rooms are present in a given entity, such as a building or dwelling.
-
E.
hasNumberOfCentres
Indicates the relationship specifying how many centers (or central units/locations) are associated with a given 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_69d87f1d32208190942e4e499a80c18c |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21ebab54c81908d82dd6a26c406c2 |
completed | April 17, 2026, 11:51 a.m. |
| PD | Predicate disambiguation | batch_69e219d642708190ba31a90dce76a210 |
completed | April 17, 2026, 11:30 a.m. |
| PDg | Predicate description generation | batch_69e21e55a2388190b29a045a8c608ba4 |
completed | April 17, 2026, 11:49 a.m. |
Created at: April 10, 2026, 5:02 a.m.