Triple
T27291794
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ksar Sghir |
E688647
|
entity |
| Predicate | distanceToTangerMed |
P199143
|
FINISHED |
| Object | approximately 12 km |
—
|
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 12 km | Statement: [Ksar Sghir, distanceToTangerMed, approximately 12 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToTangerMed Context triple: [Ksar Sghir, distanceToTangerMed, approximately 12 km]
-
A.
distanceToTunis
Indicates the spatial distance between a given entity’s location and the city of Tunis.
-
B.
distanceToTunisia
Indicates the measured or calculated distance between a given entity’s location and the country of Tunisia.
-
C.
distanceToAgadir
Indicates the spatial distance between a given entity’s location and the city of Agadir.
-
D.
distanceToTaroudant_km
Indicates the physical distance, measured in kilometers, between an entity and the location Taroudant.
-
E.
distanceToMuscat
Indicates the measured or calculated distance between a given entity’s location and the city of Muscat.
- 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_69ef355a96308190a2bed991525fb278 |
completed | April 27, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69ff234f32888190a1d800a3bda432eb |
completed | May 9, 2026, 12:06 p.m. |
| PD | Predicate disambiguation | batch_69ff228ae9a0819083f4b97c10b923f4 |
completed | May 9, 2026, 12:03 p.m. |
| PDg | Predicate description generation | batch_69ff234e63648190a7b09a161029c8e2 |
completed | May 9, 2026, 12:06 p.m. |
Created at: April 27, 2026, 11:16 a.m.