Triple
T3905589
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bab Bou Jeloud |
E87195
|
entity |
| Predicate | imageMotif |
P41002
|
FINISHED |
| Object | commonly depicted on postcards of Fez |
—
|
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: commonly depicted on postcards of Fez | Statement: [Bab Bou Jeloud, imageMotif, commonly depicted on postcards of Fez]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: imageMotif Context triple: [Bab Bou Jeloud, imageMotif, commonly depicted on postcards of Fez]
-
A.
featuresMotif
chosen
Indicates that something contains, incorporates, or prominently includes a particular recurring motif or pattern.
-
B.
usesMotifsFrom
Indicates that one entity incorporates or draws upon recurring themes, patterns, or elements that originate from another entity.
-
C.
visualFeature
Indicates a relationship where one entity possesses or exhibits a particular visual characteristic or attribute of another entity.
-
D.
evokesImageOf
Indicates that one entity triggers or brings to mind a mental image or visual representation of another entity.
-
E.
reverseMotif
Indicates that one motif is the reversed or inverted form of another motif in structure, order, or direction.
- 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_69aed9424514819086e9c58adde6652d |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef1abe2dc81909c18aeae9b286898 |
completed | March 9, 2026, 4:13 p.m. |
| PD | Predicate disambiguation | batch_69aee75cff148190b6d5979d17fae085 |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:22 p.m.