Triple
T9605261
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fehmarn |
E231953
|
entity |
| Predicate | rankInGermanyByArea |
P89198
|
FINISHED |
| Object | third-largest island in Germany |
—
|
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-largest island in Germany | Statement: [Fehmarn, rankInGermanyByArea, third-largest island in Germany]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankInGermanyByArea Context triple: [Fehmarn, rankInGermanyByArea, third-largest island in Germany]
-
A.
rankInGermanEmpireByArea
Indicates the ordinal position of an entity when all entities in the German Empire are ordered by their land area.
-
B.
rankAmongGermanStates
Indicates the relative position or standing of a German state when ordered or compared to other German states by a specific criterion (such as size, population, or performance).
-
C.
rankInWorldByArea
Indicates the position of an entity in a global ordering based on its total area size.
-
D.
rankWithinGermanStates
Indicates the relative position or standing of an entity compared to others within the set of German federal states.
-
E.
rankInRussiaByArea
Indicates the position of an entity in an ordered list of entities in Russia sorted by their area size.
- 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_69ca8484838c8190b2049199d22fef70 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9a5e4a7c8190830b5ad9762ece46 |
completed | April 1, 2026, 10:21 p.m. |
| PD | Predicate disambiguation | batch_69ccd5a6fd2481908efd131e207b8143 |
completed | April 1, 2026, 8:21 a.m. |
| PDg | Predicate description generation | batch_69ccd93fc45c8190a823305e461e581d |
completed | April 1, 2026, 8:37 a.m. |
Created at: March 30, 2026, 8:08 p.m.