Triple
T6835473
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carin von Kantzow |
E157437
|
entity |
| Predicate | relativeTypeOfFannyVonWilamowitzMoellendorff |
P73440
|
FINISHED |
| Object | sister |
—
|
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: sister | Statement: [Carin von Kantzow, relativeTypeOfFannyVonWilamowitzMoellendorff, sister]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relativeTypeOfFannyVonWilamowitzMoellendorff Context triple: [Carin von Kantzow, relativeTypeOfFannyVonWilamowitzMoellendorff, sister]
-
A.
relativeTypeToNapoleonBonaparte
Indicates the specific familial relationship that an entity has to Napoleon Bonaparte.
-
B.
relationshipWithSybilFawlty
Indicates the type or nature of a relationship that an entity has with Sybil Fawlty.
-
C.
relationshipWithMiladyDeWinter
Indicates a personal or interpersonal connection that an entity has with Milady de Winter, such as familial, romantic, adversarial, or other significant relational ties.
-
D.
relationToNurJahan
Indicates a relationship or connection that an entity has specifically to Nur Jahan.
-
E.
fictionalRelationship
Indicates a relationship that exists only within a fictional or imagined context between entities.
- 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_69c6882c53608190b99aebef079b23bd |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d67a9ff88190b0d86331b3ea06aa |
completed | March 27, 2026, 7:11 p.m. |
| PD | Predicate disambiguation | batch_69c6d09f90648190bc0a462c7d59de1b |
completed | March 27, 2026, 6:46 p.m. |
| PDg | Predicate description generation | batch_69c6d623aba88190a93ec9c83508c960 |
completed | March 27, 2026, 7:10 p.m. |
Created at: March 27, 2026, 2:19 p.m.