Triple
T17074826
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fordow enrichment facility |
E414320
|
entity |
| Predicate | distanceFromQom |
P125753
|
FINISHED |
| Object | approximately 30 kilometers |
—
|
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 30 kilometers | Statement: [Fordow enrichment facility, distanceFromQom, approximately 30 kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromQom Context triple: [Fordow enrichment facility, distanceFromQom, approximately 30 kilometers]
-
A.
distanceFromKaaba
Indicates the spatial distance between an entity’s location and the Kaaba.
-
B.
distanceFromTehran
Indicates the spatial distance between a given entity and the city of Tehran.
-
C.
distanceToPortQasim_km
Indicates the distance, measured in kilometers, from a given location to Port Qasim.
-
D.
distanceFromMedinaCenterApprox
Indicates an approximate measure of how far something is located from the center of Medina.
-
E.
distanceFromRiyadh
Indicates the measured spatial distance between a given entity’s location and the city of Riyadh.
- 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_69d886cef44c8190ba56c44b4e863e64 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dbc3b69c819093b32da3998eed46 |
completed | April 18, 2026, 7:30 p.m. |
| PD | Predicate disambiguation | batch_69e35d642f74819098c014135e249b27 |
completed | April 18, 2026, 10:31 a.m. |
| PDg | Predicate description generation | batch_69e3753f93c88190808fec5692f66699 |
completed | April 18, 2026, 12:12 p.m. |
Created at: April 10, 2026, 5:34 a.m.