Triple
T32200165
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oléron bridge |
E822509
|
entity |
| Predicate | rankInFranceByLength |
P181683
|
FINISHED |
| Object | one of the longest sea bridges in France |
—
|
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 sea bridges in France | Statement: [Oléron bridge, rankInFranceByLength, one of the longest sea bridges in France]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankInFranceByLength Context triple: [Oléron bridge, rankInFranceByLength, one of the longest sea bridges in France]
-
A.
lengthInFrance
Indicates that the specified length or duration applies specifically within the context of France (e.g., under French conditions, jurisdiction, or territory).
-
B.
rankByLengthInEurope
Indicates that entities are ordered or compared based on their length specifically within the context of Europe.
-
C.
rankingInFranceByHeight
Indicates the relative order of entities in France based on their height, from tallest to shortest or vice versa.
-
D.
rankingByLengthInSwitzerland
Indicates that entities are ordered or evaluated based on their length within the context of Switzerland.
-
E.
rankByLengthInWorld
Indicates ordering entities within a given world or context based on their length, from shortest to longest or vice versa.
- 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_69f349093174819086e633c190a51aa8 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f7805ce6208190ac6dbd9c97989978 |
completed | May 3, 2026, 5:05 p.m. |
| PD | Predicate disambiguation | batch_69f77956ec648190ba4fb7e9d83fd107 |
completed | May 3, 2026, 4:35 p.m. |
| PDg | Predicate description generation | batch_69f7805c25dc8190b9977c561ba15975 |
completed | May 3, 2026, 5:05 p.m. |
Created at: May 1, 2026, 12:36 a.m.