Triple
T21557919
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chtaura |
E531940
|
entity |
| Predicate | distanceFromBeirut_km |
P28969
|
FINISHED |
| Object | approximately 44 |
—
|
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 44 | Statement: [Chtaura, distanceFromBeirut_km, approximately 44]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromBeirut_km Context triple: [Chtaura, distanceFromBeirut_km, approximately 44]
-
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.
distanceFromDamascus
Indicates the measured distance between a given location and the city of Damascus.
-
D.
distanceToLebanonBorder
Indicates the measured or estimated spatial distance between a given location and the border of Lebanon.
-
E.
distanceFromSyriaBorder
Indicates the measured spatial separation between a location and the nearest point on Syria’s national border.
- 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_69e0c460232c81908de2c3819d17c00e |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eed2e14af88190bc70b4d0f3453aac |
completed | April 27, 2026, 3:07 a.m. |
| PD | Predicate disambiguation | batch_69e6320766308190ba5dca2f7c826aa4 |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:29 p.m.