Triple
T30693990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valday |
E781405
|
entity |
| Predicate | roadDistanceToStPetersburg_km |
P58440
|
FINISHED |
| Object | approximately 300 |
—
|
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 300 | Statement: [Valday, roadDistanceToStPetersburg_km, approximately 300]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roadDistanceToStPetersburg_km Context triple: [Valday, roadDistanceToStPetersburg_km, approximately 300]
-
A.
distanceFromSaintPetersburg
chosen
Indicates the spatial distance between a given entity and the city of Saint Petersburg.
-
B.
distanceToPetersburgInMiles
Indicates the numerical distance, measured in miles, between a given entity’s location and Petersburg.
-
C.
distanceToNizhnyNovgorod
Indicates the spatial distance between a given entity and the location of Nizhny Novgorod.
-
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_69f224ab24e08190991d6edb6df58e8b |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f68bd9ae2481908e23fbe516a03f24 |
completed | May 2, 2026, 11:42 p.m. |
| PD | Predicate disambiguation | batch_69f6861170d08190bb98be609d436f84 |
completed | May 2, 2026, 11:17 p.m. |
Created at: April 29, 2026, 8:33 p.m.