Triple
T33486238
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hassan Mosque |
E857617
|
entity |
| Predicate | nearbyUrbanFunction |
P181644
|
FINISHED |
| Object | ceremonial and state events area |
—
|
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: ceremonial and state events area | Statement: [Hassan Mosque, nearbyUrbanFunction, ceremonial and state events area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearbyUrbanFunction Context triple: [Hassan Mosque, nearbyUrbanFunction, ceremonial and state events area]
-
A.
nearbyUrbanCenter
Indicates that one location is geographically close to an urban center, such as a city or large town.
-
B.
nearbyUse
Indicates that one entity uses or operates another entity that is located nearby or in close physical proximity.
-
C.
nearbyEconomicActivity
Indicates that there is economic activity occurring in close physical proximity to the referenced entity.
-
D.
hasNearbyLandUse
Indicates that one land area is located close to another area characterized by a specific type of land use.
-
E.
nearbyActivityHub
chosen
Indicates that one entity is located close enough to another entity that serves as a central hub or focal point for activities or events.
- 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_69f3497547608190a1a0f2365fb713ee |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fe0d165a48819098b854318a50d76c |
completed | May 8, 2026, 4:19 p.m. |
| PD | Predicate disambiguation | batch_69fe0931002481908a95b34f95e9f64e |
completed | May 8, 2026, 4:02 p.m. |
Created at: May 1, 2026, 1:38 a.m.