Triple
T3569854
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Duke of Nassau |
E75541
|
entity |
| Predicate | rankWithinGermanStates |
P50606
|
FINISHED |
| Object | middle-sized principality ruler |
—
|
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: middle-sized principality ruler | Statement: [Duke of Nassau, rankWithinGermanStates, middle-sized principality ruler]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankWithinGermanStates Context triple: [Duke of Nassau, rankWithinGermanStates, middle-sized principality ruler]
-
A.
rankInGermanEmpireByArea
Indicates the ordinal position of an entity when all entities in the German Empire are ordered by their land area.
-
B.
bordersGermanState
Indicates that one entity shares a land or maritime boundary directly with a German federal state.
-
C.
countryRankContext
Indicates the relative position or ranking of a country within a specified contextual framework (such as economic, political, or performance-based criteria).
-
D.
nationalRank
Indicates the position or standing of an entity within a ranking system at the national level.
-
E.
countryRanking
Indicates the relative position or rank assigned to a country within a specific ordered list or comparative evaluation.
- 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_69ad85d512708190829c8b2d3a2ccfb8 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc0c1ecb081909051bcc1f38eea31 |
completed | March 8, 2026, 6:32 p.m. |
| PD | Predicate disambiguation | batch_69adb8364d848190a96a9bc7a6126af2 |
completed | March 8, 2026, 5:56 p.m. |
| PDg | Predicate description generation | batch_69adba25c66c81909a05a97327828c41 |
completed | March 8, 2026, 6:04 p.m. |
Created at: March 8, 2026, 3:21 p.m.