Triple
T5029929
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pitești |
E113270
|
entity |
| Predicate | distanceFromBucharest |
P32714
|
FINISHED |
| Object | approximately 120 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: approximately 120 km | Statement: [Pitești, distanceFromBucharest, approximately 120 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromBucharest Context triple: [Pitești, distanceFromBucharest, approximately 120 km]
-
A.
distanceToBucharest
chosen
Indicates the physical distance between a given location and the city of Bucharest.
-
B.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
-
C.
distanceFromRome
Indicates the measured spatial distance between a given entity’s location and the city of Rome.
-
D.
distanceFrom Tbilisi
Indicates the spatial distance between a given location or entity and the city of Tbilisi.
-
E.
distanceFromPragueKmApprox
Indicates an approximate distance, measured in kilometers, between a given entity and the city of Prague.
- 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_69bd443775e48190a646ffbfc4334723 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd739099a0819099c6201d4e1c5ee2 |
completed | March 20, 2026, 4:19 p.m. |
| PD | Predicate disambiguation | batch_69bd71509e9c8190a60c1d8d04936a12 |
completed | March 20, 2026, 4:09 p.m. |
Created at: March 20, 2026, 1:36 p.m.