Triple
T24465549
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tétouan Province |
E616956
|
entity |
| Predicate | hasUNESCOWorldHeritageCityNearby |
P16759
|
FINISHED |
| Object | Medina of Tétouan |
E49039
|
NE 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: Medina of Tétouan | Statement: [Tétouan Province, hasUNESCOWorldHeritageCityNearby, Medina of Tétouan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUNESCOWorldHeritageCityNearby Context triple: [Tétouan Province, hasUNESCOWorldHeritageCityNearby, Medina of Tétouan]
-
A.
nearbyWorldHeritageSite
chosen
Indicates that one entity is located close to, or in the immediate vicinity of, a designated World Heritage Site.
-
B.
hasNearbyCityFunction
Indicates that one entity serves as a nearby urban center or city-like service hub for another entity.
-
C.
hasNearbyCityArea
Indicates that one area is geographically close to or adjacent to a city area.
-
D.
isNearCapitalCity
Indicates that an entity is located close to, or in the immediate vicinity of, a capital city.
-
E.
hasMunicipalitySeatNearby
Indicates that the municipality’s administrative seat is located in close proximity to the referenced place or entity.
- F. None of above.
Provenance (4 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_69e2d7f197588190889a03e620558059 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f2993ecd988190991598832b29a131 |
completed | April 29, 2026, 11:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0ff84a178881908e203dc120c1f75c |
completed | May 22, 2026, 6:31 a.m. |
| PD | Predicate disambiguation | batch_69f287d76c7c81909494f12e606a9149 |
completed | April 29, 2026, 10:36 p.m. |
Created at: April 18, 2026, 2:19 a.m.