Triple
T16301689
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zimbabwe and Mozambique |
E395804
|
entity |
| Predicate | shareInternationalBoundaryLength_km |
P57957
|
FINISHED |
| Object | about 1,231 |
—
|
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 1,231 | Statement: [Zimbabwe and Mozambique, shareInternationalBoundaryLength_km, about 1,231]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: shareInternationalBoundaryLength_km Context triple: [Zimbabwe and Mozambique, shareInternationalBoundaryLength_km, about 1,231]
-
A.
shareLandBorderLengthApproxKm
chosen
Indicates that two entities share a land border whose length is approximately the given number of kilometers.
-
B.
countryBordering
Indicates that one country shares a land or maritime boundary directly with another country.
-
C.
longestLandBorderWith
Indicates that two entities share a land border and that this border is the longest land border for at least one of the entities.
-
D.
maritimeBoundaryLengthApproxKm
Indicates the approximate length, measured in kilometers, of a state's maritime boundary or coastline.
-
E.
continentBorders
Indicates that one continent shares a land or maritime boundary directly with another continent.
- 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_69d87f23bb088190a16fbb91a1957ea5 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e25e32da7081908d8bd320374a5731 |
completed | April 17, 2026, 4:22 p.m. |
| PD | Predicate disambiguation | batch_69e219fa5508819097e9d383348bf174 |
completed | April 17, 2026, 11:31 a.m. |
Created at: April 10, 2026, 5:06 a.m.