Triple
T2812847
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mariupol |
E54211
|
entity |
| Predicate | distanceToDonetsk |
P43764
|
FINISHED |
| Object | about 110 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 110 km | Statement: [Mariupol, distanceToDonetsk, about 110 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToDonetsk Context triple: [Mariupol, distanceToDonetsk, about 110 km]
-
A.
distanceToRussianBorder_km
Indicates the physical distance, measured in kilometers, between a given location and the nearest point on the Russian border.
-
B.
distanceFromMoscow_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and Moscow.
-
C.
frontLineDistanceFromMoscow
Indicates the measured distance between the current front line of a conflict and the city of Moscow.
-
D.
distanceToArkhangelskApproxKm
Indicates the approximate distance, measured in kilometers, between a given entity’s location and Arkhangelsk.
-
E.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
- 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_69ab49de0af08190b3da69683be1e728 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abde4a31d081909377044d5ff791b0 |
completed | March 7, 2026, 8:14 a.m. |
| PD | Predicate disambiguation | batch_69abdd0740208190911dc9c9546a79ae |
completed | March 7, 2026, 8:08 a.m. |
| PDg | Predicate description generation | batch_69abde0f4c648190b9812e64f30c39da |
completed | March 7, 2026, 8:13 a.m. |
Created at: March 6, 2026, 9:59 p.m.