Triple
T1962507
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ladysmith |
E42617
|
entity |
| Predicate | distanceToJohannesburg_km |
P34439
|
FINISHED |
| Object | approximately 365 |
—
|
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 365 | Statement: [Ladysmith, distanceToJohannesburg_km, approximately 365]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToJohannesburg_km Context triple: [Ladysmith, distanceToJohannesburg_km, approximately 365]
-
A.
distanceFromCapeTown
Indicates the measured distance between a given location and Cape Town.
-
B.
distanceToSouthAfrica
Indicates the measured or calculated spatial distance between a given entity and the country of South Africa.
-
C.
distanceToAdelaide_km
Indicates the physical distance, measured in kilometers, between a given location and Adelaide.
-
D.
distanceToKinshasa
Indicates the measured spatial distance between a given entity’s location and the city of Kinshasa.
-
E.
distanceToHarare
Indicates the spatial distance between a given entity and the location of Harare.
- 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_69a88711151c8190940b2572095059d7 |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb68a8e608190bc37a85913b3cd44 |
completed | March 7, 2026, 5:24 a.m. |
| PD | Predicate disambiguation | batch_69abaff5dbd48190a9d36ca60de151db |
completed | March 7, 2026, 4:56 a.m. |
| PDg | Predicate description generation | batch_69abb6893eb881908923f0168374596a |
completed | March 7, 2026, 5:24 a.m. |
Created at: March 4, 2026, 7:36 p.m.