Triple
T227335
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Congo River mouth |
E4339
|
entity |
| Predicate | hasApproximateWidth |
P3989
|
FINISHED |
| Object | about 10 to 20 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: about 10 to 20 kilometers | Statement: [Congo River mouth, hasApproximateWidth, about 10 to 20 kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateWidth Context triple: [Congo River mouth, hasApproximateWidth, about 10 to 20 kilometers]
-
A.
hasWidth
chosen
Indicates that an entity possesses a specific measurement or extent along its width dimension.
-
B.
approximateSize
Indicates that one entity has a size that is roughly or approximately equal to the size of another entity.
-
C.
hasApproximateDuration
Indicates that one entity has a duration that is estimated or not exact, typically expressed as an approximate length of time.
-
D.
hasApproximateNumberOfLetters
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
-
E.
approximateDiameter
Indicates that one entity specifies the estimated or rough measurement of another entity’s diameter.
- 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_69a257363ffc81909757bde7ab3404da |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25d10ac248190a98dedabf5358668 |
completed | Feb. 28, 2026, 3:12 a.m. |
| PD | Predicate disambiguation | batch_69a25b5877588190af694d060377f027 |
completed | Feb. 28, 2026, 3:04 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.