Triple
T12953390
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ceduna |
E309948
|
entity |
| Predicate | distanceFromAdelaideByRoad_km |
P27723
|
FINISHED |
| Object | approximately 770 |
—
|
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 770 | Statement: [Ceduna, distanceFromAdelaideByRoad_km, approximately 770]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromAdelaideByRoad_km Context triple: [Ceduna, distanceFromAdelaideByRoad_km, approximately 770]
-
A.
distanceToAdelaide_km
chosen
Indicates the physical distance, measured in kilometers, between a given location and Adelaide.
-
B.
distanceFromWaggaWagga_km
Indicates the numerical distance, measured in kilometers, between an entity’s location and Wagga Wagga.
-
C.
distanceFromDarwin
Indicates the spatial distance between an entity and the location Darwin.
-
D.
distanceToKarratha_km
Indicates the distance, measured in kilometers, from a given location to Karratha.
-
E.
distanceToDarwin
Indicates the spatial distance between a given entity’s location and the location of Darwin.
- 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_69d7bdfb57a88190836b743e2825feca |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97e59a4c88190907d05b8d57dae89 |
completed | April 10, 2026, 10:48 p.m. |
| PD | Predicate disambiguation | batch_69d97dba57988190b786ffed55687a72 |
completed | April 10, 2026, 10:46 p.m. |
Created at: April 9, 2026, 5:44 p.m.