Triple
T17262644
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mohammedia railway station |
E419043
|
entity |
| Predicate | hasApproximateDirectionFromCasablanca |
P126776
|
FINISHED |
| Object | northeast |
—
|
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: northeast | Statement: [Mohammedia railway station, hasApproximateDirectionFromCasablanca, northeast]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateDirectionFromCasablanca Context triple: [Mohammedia railway station, hasApproximateDirectionFromCasablanca, northeast]
-
A.
previousTravelTimeTangierCasablanca
Indicates the duration it previously took to travel from Tangier to Casablanca.
-
B.
travelTimeTangierCasablancaBefore
Indicates that the travel time from Tangier to Casablanca occurs before a specified reference time or event.
-
C.
travelTimeTangierCasablancaAfter
Indicates the duration of travel from Tangier to Casablanca occurring after a specified reference time or event.
-
D.
directionFromMogadishu
Indicates the cardinal or relative direction in which one entity is located when measured from Mogadishu as the point of reference.
-
E.
distanceFromMarrakesh
Indicates the spatial distance between a given location and the city of Marrakesh.
- 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_69d886d9ab108190b70edd8d17aa1204 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42e717a348190ae6835fb08f38125 |
completed | April 19, 2026, 1:22 a.m. |
| PD | Predicate disambiguation | batch_69e3832a284481908a8a3da7ac91de5a |
completed | April 18, 2026, 1:12 p.m. |
| PDg | Predicate description generation | batch_69e39c2fedb881908bfed2c3e5f2616a |
completed | April 18, 2026, 2:58 p.m. |
Created at: April 10, 2026, 5:40 a.m.