Triple
T25069418
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Philina |
E627867
|
entity |
| Predicate | relationshipTypeWithWilhelm Meister |
P174649
|
FINISHED |
| Object | friend |
—
|
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: friend | Statement: [Philina, relationshipTypeWithWilhelm Meister, friend]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithWilhelm Meister Context triple: [Philina, relationshipTypeWithWilhelm Meister, friend]
-
A.
relationshipToGustav von Aschenbach
Indicates the specific type of personal, social, or emotional connection an entity has to Gustav von Aschenbach.
-
B.
relationshipType
Indicates the specific kind of relationship that exists between two or more entities.
-
C.
relationshipTypeWithSallyBowles
Indicates the specific nature or category of relationship that an entity has with Sally Bowles.
-
D.
relationshipTypeWithHelenSchlegel
Indicates the specific nature or category of relationship that an entity has with Helen Schlegel.
-
E.
literaryRelationship
Indicates a relationship between entities that are connected through literature, such as authorship, influence, adaptation, or other text-based associations.
- 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_69e2ff2d71dc8190b4758e57d643cbe4 |
completed | April 18, 2026, 3:49 a.m. |
| NER | Named-entity recognition | batch_69f6c5b7e46081909975b05f7298cc0e |
completed | May 3, 2026, 3:49 a.m. |
| PD | Predicate disambiguation | batch_69f6c3f23ae081909a52801266063a3c |
completed | May 3, 2026, 3:41 a.m. |
| PDg | Predicate description generation | batch_69f6c49069e48190a3486b6254a6645b |
completed | May 3, 2026, 3:44 a.m. |
Created at: April 18, 2026, 6:10 a.m.