Triple
T18417189
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Erlend Nikulaussøn |
E441922
|
entity |
| Predicate | relationshipTypeWithKristinLavransdatter |
P131526
|
FINISHED |
| Object | passionate |
—
|
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: passionate | Statement: [Erlend Nikulaussøn, relationshipTypeWithKristinLavransdatter, passionate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithKristinLavransdatter Context triple: [Erlend Nikulaussøn, relationshipTypeWithKristinLavransdatter, passionate]
-
A.
relationshipToKristinSquires
Indicates the nature or type of relationship an entity has with Kristin Squires.
-
B.
hasRelationshipTypeWith Alexandra Bergson
Indicates that there exists a specific type or category of relationship between an entity and Alexandra Bergson.
-
C.
literaryRelationship
Indicates a relationship between entities that are connected through literature, such as authorship, influence, adaptation, or other text-based associations.
-
D.
relationshipStatusWithKrista
Indicates the current nature or state of an entity’s relationship with Krista.
-
E.
relationshipTypeWithStephanie Ramzinski
Indicates the specific nature or category of relationship that an entity has with Stephanie Ramzinski.
- 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_69d8b9eb8a508190a942fd75ebd8b1dc |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e51a284b608190b77c360a72aceb7a |
completed | April 19, 2026, 6:08 p.m. |
| PD | Predicate disambiguation | batch_69e469bf7f74819096a01173493412c2 |
completed | April 19, 2026, 5:35 a.m. |
| PDg | Predicate description generation | batch_69e46d2aa72c8190a40854a7a52081e2 |
completed | April 19, 2026, 5:50 a.m. |
Created at: April 10, 2026, 10:47 a.m.