Triple
T13430210
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kwa Falls |
E313586
|
entity |
| Predicate | distanceFromCalabar |
P109895
|
FINISHED |
| Object | approximately 25–30 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 25–30 kilometers | Statement: [Kwa Falls, distanceFromCalabar, approximately 25–30 kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromCalabar Context triple: [Kwa Falls, distanceFromCalabar, approximately 25–30 kilometers]
-
A.
distanceFromCuenca
Indicates the measured spatial distance between a given entity or location and the city of Cuenca.
-
B.
distanceFromBeninCity_km
Indicates the distance, measured in kilometers, between a given location and Benin City.
-
C.
distanceFromCayenne
Indicates the measured distance between a given entity or location and the place named Cayenne.
-
D.
distanceToAbuja
Indicates the spatial distance between a given location or entity and the city of Abuja.
-
E.
approxDistanceFromCebuCity
Indicates that one entity is located at an approximate distance from Cebu City.
- 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_69d806ad0c44819088833ae1ec9e9690 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaed304ac8190a8021f749de8164c |
completed | April 12, 2026, 2:40 p.m. |
| PD | Predicate disambiguation | batch_69d9a03926188190ab3948d1f5d3941f |
completed | April 11, 2026, 1:13 a.m. |
| PDg | Predicate description generation | batch_69dadcce5a808190847f2a7833b67a5a |
completed | April 11, 2026, 11:44 p.m. |
Created at: April 9, 2026, 9:40 p.m.