Triple
T10202857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sharurah |
E238923
|
entity |
| Predicate | distanceToYemeniBorderApproxKm |
P92692
|
FINISHED |
| Object | tens of 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: tens of kilometers | Statement: [Sharurah, distanceToYemeniBorderApproxKm, tens of kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToYemeniBorderApproxKm Context triple: [Sharurah, distanceToYemeniBorderApproxKm, tens of kilometers]
-
A.
distanceFromSanaa
Indicates the spatial distance between an entity and the location of Sanaa.
-
B.
borderTypeWithYemen
Indicates the type or nature of the border that exists between a given entity and Yemen.
-
C.
distanceFromSyriaBorder
Indicates the measured spatial separation between a location and the nearest point on Syria’s national border.
-
D.
distanceToLebanonBorder
Indicates the measured or estimated spatial distance between a given location and the border of Lebanon.
-
E.
distanceFromJordanBorder
Indicates the measured spatial distance between a given location and the border of Jordan.
- 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_69ca84e1ea088190b38162e43d4cfa8f |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdee4239c08190b40f5cc19c3db3c7 |
completed | April 2, 2026, 4:19 a.m. |
| PD | Predicate disambiguation | batch_69cd7c8b7f348190a585daa70de4d4c8 |
completed | April 1, 2026, 8:14 p.m. |
| PDg | Predicate description generation | batch_69cd7edc6cf081909d95859d880a4059 |
completed | April 1, 2026, 8:23 p.m. |
Created at: March 30, 2026, 9:14 p.m.