Triple
T19932287
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aït Benhaddou |
E479083
|
entity |
| Predicate | distanceToOuarzazate |
P137890
|
FINISHED |
| Object | approximately 30 kilometers |
—
|
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: approximately 30 kilometers | Statement: [Aït Benhaddou, distanceToOuarzazate, approximately 30 kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToOuarzazate Context triple: [Aït Benhaddou, distanceToOuarzazate, approximately 30 kilometers]
-
A.
distanceFromMarrakesh
Indicates the spatial distance between a given location and the city of Marrakesh.
-
B.
distanceToAgadir
Indicates the spatial distance between a given entity’s location and the city of Agadir.
-
C.
distanceToTunis
Indicates the spatial distance between a given entity’s location and the city of Tunis.
-
D.
distanceFromTinghir
Indicates the spatial distance between a given entity’s location and the location of Tinghir.
-
E.
distanceToBizerte
Indicates the measured distance between a given entity’s location and the city of Bizerte.
- F. None of above. chosen
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_69d8e521855c8190b41871700afc8d6a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65a14a2348190b41f8e320fe661d6 |
completed | April 20, 2026, 4:53 p.m. |
| PD | Predicate disambiguation | batch_69e537f070b481908958e0e5911dcdc1 |
completed | April 19, 2026, 8:15 p.m. |
| PDg | Predicate description generation | batch_69e543c136b081909cab9394b958390a |
completed | April 19, 2026, 9:06 p.m. |
Created at: April 10, 2026, 1:53 p.m.