Triple
T18290519
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beslan |
E438099
|
entity |
| Predicate | distanceToVladikavkaz_km |
P70181
|
FINISHED |
| Object | about 15 |
—
|
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 15 | Statement: [Beslan, distanceToVladikavkaz_km, about 15]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToVladikavkaz_km Context triple: [Beslan, distanceToVladikavkaz_km, about 15]
-
A.
distanceToVladikavkaz
chosen
Indicates the spatial distance between a given entity and the city of Vladikavkaz.
-
B.
distanceToGrozny_km
Indicates the physical distance, measured in kilometers, between a given location and the city of Grozny.
-
C.
distanceToRostovOnDon_km
Indicates the physical distance, measured in kilometers, between an entity’s location and the city of Rostov-on-Don.
-
D.
distanceFromMoscow_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and Moscow.
-
E.
distanceToSaratov_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Saratov.
- 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_69d8b914530c8190b4474d862a2b2a1b |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e500fee5248190928e68ddaa4d90d7 |
completed | April 19, 2026, 4:21 p.m. |
| PD | Predicate disambiguation | batch_69e44fdf43d08190bbcfb6b1fe3cc0ee |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:35 a.m.