Triple
T26161759
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Keserwan District |
E654128
|
entity |
| Predicate | distanceToBeirutApprox |
P28969
|
FINISHED |
| Object | about 20 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: about 20 km | Statement: [Keserwan District, distanceToBeirutApprox, about 20 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToBeirutApprox Context triple: [Keserwan District, distanceToBeirutApprox, about 20 km]
-
A.
distanceFromBeirut
chosen
Indicates the measured spatial distance between a given entity’s location and the city of Beirut.
-
B.
distanceToLebanon
Indicates the measured or estimated spatial distance between a given entity’s location and the country of Lebanon.
-
C.
distanceToLebanonBorder
Indicates the measured or estimated spatial distance between a given location and the border of Lebanon.
-
D.
drivingTimeFromBeirut
Indicates the amount of time it takes to drive from Beirut to a given location.
-
E.
distanceFromDamascus
Indicates the measured distance between a given location and the city of Damascus.
- F. None of above.
Provenance (3 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_69ee5b44391c81908bdbd8813ba9aa99 |
completed | April 26, 2026, 6:36 p.m. |
| NER | Named-entity recognition | batch_69fd974d75e08190af46b1d608769f3b |
completed | May 8, 2026, 7:57 a.m. |
| PD | Predicate disambiguation | batch_69fd94ff792c8190bedf4a639d3da809 |
completed | May 8, 2026, 7:47 a.m. |
Created at: April 26, 2026, 8:30 p.m.