Triple
T31023114
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monastery of Sant Pere de Rodes |
E790500
|
entity |
| Predicate | isRuin |
P32402
|
FINISHED |
| Object | partially restored monastery complex |
—
|
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: partially restored monastery complex | Statement: [Monastery of Sant Pere de Rodes, isRuin, partially restored monastery complex]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isRuin Context triple: [Monastery of Sant Pere de Rodes, isRuin, partially restored monastery complex]
-
A.
hasRuin
chosen
Indicates that one entity possesses, contains, or is associated with a ruin or ruined structure.
-
B.
hasRuinsOn
Indicates that one location or object contains or is the site of ruins situated upon it.
-
C.
cityRuinsOf
Indicates that a city is located on or associated with the ruins of another, earlier city.
-
D.
hasRuinsSharedWith
Indicates that two or more entities share the same ruins or archaeological remains in common.
-
E.
hasCauseOfDestruction
Indicates that one entity is the cause or agent responsible for the destruction or damage 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_69f224c811508190a7de096a5b1f5798 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f6953bafb88190a860e9c68a3dd4b2 |
completed | May 3, 2026, 12:22 a.m. |
| PD | Predicate disambiguation | batch_69f690ef92308190903a54fc74233269 |
completed | May 3, 2026, 12:03 a.m. |
Created at: April 29, 2026, 8:58 p.m.