Triple
T18095655
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Principality of Quedlinburg |
E433077
|
entity |
| Predicate | hasAbbess |
P130407
|
FINISHED |
| Object | imperial princess |
—
|
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: imperial princess | Statement: [Principality of Quedlinburg, hasAbbess, imperial princess]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAbbess Context triple: [Principality of Quedlinburg, hasAbbess, imperial princess]
-
A.
abbeyConsecrated
Indicates that an abbey has been formally dedicated and made sacred for religious use through a consecration ceremony.
-
B.
hadAbbot
Indicates that an institution, typically a monastery or abbey, was under the leadership or authority of a specific abbot.
-
C.
hasTerritorialAbbacy
Indicates that an entity holds jurisdiction as a territorial abbacy over a specific geographic area.
-
D.
hadAbbey
Indicates that an entity possessed, was associated with, or was the site of a particular abbey.
-
E.
abbeySuccessor
Indicates that one abbey succeeds or follows another in a sequence, lineage, or position.
- 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_69d8b907d05c819083cc3bd6021089e6 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4dd1c56848190be0b8c80b30dba6c |
completed | April 19, 2026, 1:48 p.m. |
| PD | Predicate disambiguation | batch_69e4330e1f2881908b2506d47c48736b |
completed | April 19, 2026, 1:42 a.m. |
| PDg | Predicate description generation | batch_69e438f5ae2c8190b11dee46534fa5a9 |
completed | April 19, 2026, 2:07 a.m. |
Created at: April 10, 2026, 10:27 a.m.