Triple
T25020076
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Winnie Verloc |
E626546
|
entity |
| Predicate | relationshipToAdolfVerloc |
P171377
|
FINISHED |
| Object | emotionally dependent |
—
|
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: emotionally dependent | Statement: [Winnie Verloc, relationshipToAdolfVerloc, emotionally dependent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToAdolfVerloc Context triple: [Winnie Verloc, relationshipToAdolfVerloc, emotionally dependent]
-
A.
relationshipToBaron de Wolmar
Indicates the nature or type of connection, association, or role that one entity has in relation to Baron de Wolmar.
-
B.
relationshipToVeronika
Indicates the specific type of personal, social, or familial relationship that one entity has to Veronika.
-
C.
relationshipToManfred
Indicates the specific type of relationship or connection that one entity has to the individual named Manfred.
-
D.
relationToVonMaur
Indicates a specified type of relationship or association that an entity has with Von Maur.
-
E.
relationshipToHermann
Indicates the specific familial, social, or professional relationship that one entity has to the person named Hermann.
- 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_69e2ff28ee3881909c626af002457a4a |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f69f80b62c8190bf2af2be0d3a7df8 |
completed | May 3, 2026, 1:06 a.m. |
| PD | Predicate disambiguation | batch_69f69d17e8d48190b30bcc2f4bd81eb2 |
completed | May 3, 2026, 12:55 a.m. |
| PDg | Predicate description generation | batch_69f69edae2448190925ce701c8792c52 |
completed | May 3, 2026, 1:03 a.m. |
Created at: April 18, 2026, 6:06 a.m.