Triple
T2338998
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guadalquivir River |
E44378
|
entity |
| Predicate | rankByLengthInSpain |
P18229
|
FINISHED |
| Object | one of the longest rivers in Spain |
—
|
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: one of the longest rivers in Spain | Statement: [Guadalquivir River, rankByLengthInSpain, one of the longest rivers in Spain]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankByLengthInSpain Context triple: [Guadalquivir River, rankByLengthInSpain, one of the longest rivers in Spain]
-
A.
rankByLengthInEurope
Indicates that entities are ordered or compared based on their length specifically within the context of Europe.
-
B.
rankByLengthInWorld
Indicates ordering entities within a given world or context based on their length, from shortest to longest or vice versa.
-
C.
rankInCanaryIslandsByArea
Indicates the numerical position of an entity in an ordered list of areas within the Canary Islands, from largest to smallest.
-
D.
rankByLength
chosen
Indicates ordering a set of items based on their length, typically from shortest to longest or vice versa.
-
E.
hasNameInSpanish
Indicates that an entity is associated with a specific name expressed in the Spanish language.
- 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_69a889132b488190bbb43ad4780ddd92 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abc6f75d888190a2e41edaa532e83f |
completed | March 7, 2026, 6:34 a.m. |
| PD | Predicate disambiguation | batch_69abc594087c819098100a10c5478a4b |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 4, 2026, 7:51 p.m.