Triple
T16416154
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Suzdal |
E398689
|
entity |
| Predicate | distanceFromVladimir |
P123343
|
FINISHED |
| Object | about 35 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 35 km | Statement: [Suzdal, distanceFromVladimir, about 35 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromVladimir Context triple: [Suzdal, distanceFromVladimir, about 35 km]
-
A.
distanceToVladikavkaz
Indicates the spatial distance between a given entity and the city of Vladikavkaz.
-
B.
distanceFromMoscow_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and Moscow.
-
C.
distanceToVladivostok_km
Indicates the physical distance, measured in kilometers, between a given location and Vladivostok.
-
D.
distanceToRostovOnDon_km
Indicates the physical distance, measured in kilometers, between an entity’s location and the city of Rostov-on-Don.
-
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. 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_69d87f2b9024819085c20e52de95d583 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32877ff248190886717d3329421a7 |
completed | April 18, 2026, 6:45 a.m. |
| PD | Predicate disambiguation | batch_69e226fe1dd08190865c181721f8c348 |
completed | April 17, 2026, 12:26 p.m. |
| PDg | Predicate description generation | batch_69e24555bb6c8190977cf5c5f9149056 |
completed | April 17, 2026, 2:36 p.m. |
Created at: April 10, 2026, 5:09 a.m.