Triple
T37671250
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elena Lincoln |
E937962
|
entity |
| Predicate | relationshipTypeWithChristianGrey |
P10690
|
FINISHED |
| Object | former lover |
—
|
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: former lover | Statement: [Elena Lincoln, relationshipTypeWithChristianGrey, former lover]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithChristianGrey Context triple: [Elena Lincoln, relationshipTypeWithChristianGrey, former lover]
-
A.
hasRelationshipTypeWith Anastasia Steele
Indicates that an entity has a specific type of relationship or relational role with Anastasia Steele.
-
B.
sexualRelationshipTo
Indicates that one entity has engaged in a sexual relationship or sexual activity with another entity.
-
C.
relationshipWithHumbertHumbert
Indicates that an entity has a specified type of personal, emotional, or social relationship with Humbert Humbert.
-
D.
literaryRelationship
Indicates a relationship between entities that are connected through literature, such as authorship, influence, adaptation, or other text-based associations.
-
E.
relationshipType
chosen
Indicates the specific kind of relationship that exists between two or more entities.
- 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_69f76ed7b1408190ba8c93c53cb8becf |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a037c8efcd4819088c2aeead65d93df |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a1772e48190ba738c6d11b321e2 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:18 p.m.