Triple
T4439829
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Adige |
E95742
|
entity |
| Predicate | rankingByLengthInItaly |
P56037
|
FINISHED |
| Object | second-longest river in Italy |
—
|
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: second-longest river in Italy | Statement: [Adige, rankingByLengthInItaly, second-longest river in Italy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankingByLengthInItaly Context triple: [Adige, rankingByLengthInItaly, second-longest river in Italy]
-
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.
rankingByLengthInSwitzerland
Indicates that entities are ordered or evaluated based on their length within the context of Switzerland.
-
D.
rankingByLengthInChina
Indicates that entities are ordered or evaluated based on their length within the context of China.
-
E.
designationInItaly
Indicates that an entity holds a specific official status, title, or classification within the Italian context (e.g., legal, administrative, or honorific designation in Italy).
- F. None of above. chosen
Provenance (4 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_69b3453ea2b48190a26f154b3b8fece5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b355aaa9288190b95d875d343d6ee5 |
completed | March 13, 2026, 12:09 a.m. |
| PD | Predicate disambiguation | batch_69b34f6078cc8190831b89f404198cc5 |
completed | March 12, 2026, 11:42 p.m. |
| PDg | Predicate description generation | batch_69b3505a87b4819083fbbd58870e520b |
completed | March 12, 2026, 11:46 p.m. |
Created at: March 12, 2026, 11:31 p.m.