Triple
T5298436
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santa Catalina Monastery |
E119912
|
entity |
| Predicate | cityWithinCityLayout |
P48747
|
FINISHED |
| Object | maze-like streets |
—
|
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: maze-like streets | Statement: [Santa Catalina Monastery, cityWithinCityLayout, maze-like streets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cityWithinCityLayout Context triple: [Santa Catalina Monastery, cityWithinCityLayout, maze-like streets]
-
A.
urbanLayout
chosen
Indicates how the spatial arrangement, organization, and structure of buildings, streets, and public spaces relate to one another within an urban area.
-
B.
cityPanorama
Indicates a wide, comprehensive visual view or representation of a cityscape, typically encompassing many of its features in a single scene.
-
C.
hasStreetLayoutCenteredOn
Indicates that the spatial organization or pattern of streets in one place is arranged with a particular feature or location as its central focus or reference point.
-
D.
cityService
Indicates that a service is provided by, operates within, or is administered by a particular city.
-
E.
cityWide
Indicates that something applies to, affects, or extends across an entire city.
- 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_69bd446f22b88190b6a47fb91c68a3e7 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd8e44e7c881909b241b2fec366038 |
completed | March 20, 2026, 6:13 p.m. |
| PD | Predicate disambiguation | batch_69bd845097ac81909678624c4907fda4 |
completed | March 20, 2026, 5:30 p.m. |
Created at: March 20, 2026, 1:53 p.m.