Triple
T8762420
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tomsk |
E208233
|
entity |
| Predicate | distanceFromMoscowApproximate_km |
P24098
|
FINISHED |
| Object | 2800 |
—
|
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: 2800 | Statement: [Tomsk, distanceFromMoscowApproximate_km, 2800]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromMoscowApproximate_km Context triple: [Tomsk, distanceFromMoscowApproximate_km, 2800]
-
A.
distanceFromMoscow_km
chosen
Indicates the physical distance, measured in kilometers, between a given entity’s location and Moscow.
-
B.
railDistanceFromMoscowCenter_km
Indicates the distance in kilometers from the center of Moscow to a location when traveling by rail.
-
C.
distanceFromSaintPetersburg
Indicates the spatial distance between a given entity and the city of Saint Petersburg.
-
D.
distanceToArkhangelskApproxKm
Indicates the approximate distance, measured in kilometers, between a given entity’s location and Arkhangelsk.
-
E.
distanceToGrozny_km
Indicates the physical distance, measured in kilometers, between a given location and the city of Grozny.
- 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_69ca835df7e08190ac875664cca8f9ca |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5dfc85e481909a7ce80c5022e6e9 |
completed | March 31, 2026, 11:51 p.m. |
| PD | Predicate disambiguation | batch_69cc5c1884bc8190a46e8308db31f7ab |
completed | March 31, 2026, 11:43 p.m. |
Created at: March 30, 2026, 6:40 p.m.