Triple
T9092243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Novocherkassk |
E217917
|
entity |
| Predicate | distanceToRostovOnDon_km |
P87129
|
FINISHED |
| Object | approximately 40 |
—
|
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 40 | Statement: [Novocherkassk, distanceToRostovOnDon_km, approximately 40]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToRostovOnDon_km Context triple: [Novocherkassk, distanceToRostovOnDon_km, approximately 40]
-
A.
distanceFromMoscow_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and Moscow.
-
B.
distanceFromSaintPetersburg
Indicates the spatial distance between a given entity and the city of Saint Petersburg.
-
C.
distanceToGrozny_km
Indicates the physical distance, measured in kilometers, between a given location and the city of Grozny.
-
D.
distanceToVladikavkaz
Indicates the spatial distance between a given entity and the city of Vladikavkaz.
-
E.
railDistanceFromMoscowCenter_km
Indicates the distance in kilometers from the center of Moscow to a location when traveling by rail.
- 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_69ca83d8ab5881909d8fddae363b32b1 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc96b18b24819097b525ddad3a85c0 |
completed | April 1, 2026, 3:53 a.m. |
| PD | Predicate disambiguation | batch_69cc65fc7f408190a5846e29ab3b97e5 |
completed | April 1, 2026, 12:25 a.m. |
| PDg | Predicate description generation | batch_69cc6a3c78388190a7436acc0e44ff55 |
completed | April 1, 2026, 12:43 a.m. |
Created at: March 30, 2026, 7:14 p.m.