Triple
T5151969
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Urgench |
E116214
|
entity |
| Predicate | distanceToKhiva |
P62252
|
FINISHED |
| Object | about 30 kilometres |
—
|
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 30 kilometres | Statement: [Urgench, distanceToKhiva, about 30 kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToKhiva Context triple: [Urgench, distanceToKhiva, about 30 kilometres]
-
A.
distanceFromSamarkand_km
Indicates the physical distance, measured in kilometers, between a given place or entity and the city of Samarkand.
-
B.
distanceFromAlmaty_km
Indicates the distance, measured in kilometers, between a given place or object and the city of Almaty.
-
C.
distanceToPersepolis
Indicates the measured spatial distance between a given entity and the location of Persepolis.
-
D.
distanceFromMoscow_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and Moscow.
-
E.
distanceFromAshgabat
Indicates the measured spatial distance between a given location and the city of Ashgabat.
- 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_69bd445d94788190b72e2cc563120995 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd79c1354c81908176703b4853c1a4 |
completed | March 20, 2026, 4:45 p.m. |
| PD | Predicate disambiguation | batch_69bd77b0fbb88190851e2d7ae1bdcc09 |
completed | March 20, 2026, 4:37 p.m. |
| PDg | Predicate description generation | batch_69bd79bf9b088190a556dc02f10204e4 |
completed | March 20, 2026, 4:45 p.m. |
Created at: March 20, 2026, 1:44 p.m.