Triple
T1272312
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Niger River |
E15736
|
entity |
| Predicate | rankByLengthInAfrica |
P16563
|
FINISHED |
| Object | third-longest river in Africa |
—
|
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: third-longest river in Africa | Statement: [Niger River, rankByLengthInAfrica, third-longest river in Africa]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankByLengthInAfrica Context triple: [Niger River, rankByLengthInAfrica, third-longest river in Africa]
-
A.
rankInAfricaByLength
chosen
Indicates the position of something in an ordered list of African entities sorted by their length (e.g., size, distance, or extent).
-
B.
rankByLengthInWorld
Indicates ordering entities within a given world or context based on their length, from shortest to longest or vice versa.
-
C.
rankByLengthInEurope
Indicates that entities are ordered or compared based on their length specifically within the context of Europe.
-
D.
rankByLengthInAsia
Indicates that entities are ordered or compared based on their length within the context of Asia.
-
E.
rankByLength
Indicates ordering a set of items based on their length, typically from shortest to longest or vice versa.
- 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_69a4935a94308190bb92555b79032824 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4c06c033081909bc594157abaf5bb |
completed | March 1, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69a4bede52a081909665d60acbe41d31 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:50 p.m.