Triple
T33732505
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Münster im Elsass |
E864308
|
entity |
| Predicate | hatNameAbgeleitetVon |
P171853
|
FINISHED |
| Object | lateinisch monasterium (Kloster) |
—
|
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: lateinisch monasterium (Kloster) | Statement: [Münster im Elsass, hatNameAbgeleitetVon, lateinisch monasterium (Kloster)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hatNameAbgeleitetVon Context triple: [Münster im Elsass, hatNameAbgeleitetVon, lateinisch monasterium (Kloster)]
-
A.
hatAmtsbezeichnungIn
Indicates that an entity holds or is associated with a specific official title or designation within a given context.
-
B.
hatName
Indicates that an entity has or is associated with a hat identified by a specific name.
-
C.
hatAnderenNamen
chosen
Indicates that an entity is known or referred to by another name (i.e., it has an alternative or different name).
-
D.
hatAmtseidNach
Indicates that one entity has followed or succeeded another in time or sequence.
-
E.
hadEponymousAncestor
Indicates that an entity has an ancestor whose name it shares or from whom its own name is derived.
- F. None of above.
Provenance (3 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_69f3498a64cc8190b4b414c67b280d93 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6fb1fcda08190a503098914ba09ab |
completed | May 3, 2026, 7:37 a.m. |
| PD | Predicate disambiguation | batch_69f6f96dd4c8819093d6a7bd046a9ad5 |
completed | May 3, 2026, 7:29 a.m. |
Created at: May 1, 2026, 1:44 a.m.