Triple
T14020606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Igreja Matriz de São Clemente |
E337318
|
entity |
| Predicate | hasNaveCount |
P17652
|
FINISHED |
| Object | three naves |
—
|
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: three naves | Statement: [Igreja Matriz de São Clemente, hasNaveCount, three naves]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNaveCount Context triple: [Igreja Matriz de São Clemente, hasNaveCount, three naves]
-
A.
hasNave
Indicates that one entity (typically a building or structure) possesses or includes a nave as a distinct architectural part.
-
B.
hasNavePlan
Indicates that an entity possesses or is associated with a specific plan or layout for a nave (the central part of a church or similar building).
-
C.
numberOfNaves
chosen
Indicates the specific count of naves (longitudinal sections) that a building, typically a church, possesses.
-
D.
hasPylonCount
Indicates the relationship specifying how many pylons are associated with or present in a given entity.
-
E.
naveWidthInMeters
Indicates the width of a nave, measured in meters.
- 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_69d81c6543a48190bd5ba93d7419e797 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2f3c7cd88190b236382058581740 |
completed | April 14, 2026, 12:12 p.m. |
| PD | Predicate disambiguation | batch_69de05a802ac819090604025aae6a4d5 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 9, 2026, 10:19 p.m.