Triple
T23971175
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chipata |
E604235
|
entity |
| Predicate | borderDistanceToMalawi_km |
P154080
|
FINISHED |
| Object | about 20 |
—
|
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: about 20 | Statement: [Chipata, borderDistanceToMalawi_km, about 20]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: borderDistanceToMalawi_km Context triple: [Chipata, borderDistanceToMalawi_km, about 20]
-
A.
distanceFromMbabaneKilometers
Indicates the physical distance, measured in kilometers, between an entity’s location and the city of Mbabane.
-
B.
regionWithinMozambique
Indicates that one region is geographically located within the national boundaries of Mozambique.
-
C.
distanceToBangladeshBorder_km
Indicates the distance, measured in kilometers, from a given location to the nearest point on the border of Bangladesh.
-
D.
borderTypeWithKenya
Indicates the type or nature of the border relationship that an entity shares with Kenya.
-
E.
distanceFromLusaka
Indicates the spatial distance between a given location and the city of Lusaka.
- 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_69e29543019c8190872462e593cc50b4 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f1d1dc3f088190a55faf6f01ddf4bf |
completed | April 29, 2026, 9:39 a.m. |
| PD | Predicate disambiguation | batch_69f161578d54819084a8b35496299993 |
completed | April 29, 2026, 1:39 a.m. |
| PDg | Predicate description generation | batch_69f167dca3608190ace9d2eef56b2af6 |
completed | April 29, 2026, 2:07 a.m. |
Created at: April 17, 2026, 9:25 p.m.