Triple
T10597254
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fort Capuzzo |
E250146
|
entity |
| Predicate | distanceToEgyptianBorder |
P94849
|
FINISHED |
| Object | a few kilometres |
—
|
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: a few kilometres | Statement: [Fort Capuzzo, distanceToEgyptianBorder, a few kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToEgyptianBorder Context triple: [Fort Capuzzo, distanceToEgyptianBorder, a few kilometres]
-
A.
distanceFromSyriaBorder
Indicates the measured spatial separation between a location and the nearest point on Syria’s national border.
-
B.
distanceFromJordanBorder
Indicates the measured spatial distance between a given location and the border of Jordan.
-
C.
distanceToLebanonBorder
Indicates the measured or estimated spatial distance between a given location and the border of Lebanon.
-
D.
distanceToCairo_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Cairo.
-
E.
distanceFromCairo
Indicates the measured spatial distance between a given entity’s location and the city of Cairo.
- 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_69d381c9d3d48190a29ee491e1696a0e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d5278e5d5481908f4e19c52bd80327 |
completed | April 7, 2026, 3:49 p.m. |
| PD | Predicate disambiguation | batch_69d5190a6dfc819088262957551808a1 |
completed | April 7, 2026, 2:47 p.m. |
| PDg | Predicate description generation | batch_69d5270eca0481908573b698390c5b08 |
completed | April 7, 2026, 3:47 p.m. |
Created at: April 6, 2026, 12:41 p.m.