Triple
T12754610
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Belgium and France |
E304824
|
entity |
| Predicate | sharesLandBorderLengthApproxKm |
P57957
|
FINISHED |
| Object | 620 |
—
|
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: 620 | Statement: [Belgium and France, sharesLandBorderLengthApproxKm, 620]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sharesLandBorderLengthApproxKm Context triple: [Belgium and France, sharesLandBorderLengthApproxKm, 620]
-
A.
shareLandBorderLengthApproxKm
chosen
Indicates that two entities share a land border whose length is approximately the given number of kilometers.
-
B.
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.
-
C.
countryBordering
Indicates that one country shares a land or maritime boundary directly with another country.
-
D.
continentBorders
Indicates that one continent shares a land or maritime boundary directly with another continent.
-
E.
countryBorderType
Indicates the type or nature of the border relationship that exists between two countries.
- 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_69d7bdf1fcd081909ffb0e0d6fa3a07d |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96d89ea70819098c470344f172167 |
completed | April 10, 2026, 9:37 p.m. |
| PD | Predicate disambiguation | batch_69d96406e97c8190b79081039847115c |
completed | April 10, 2026, 8:56 p.m. |
Created at: April 9, 2026, 5:27 p.m.