Triple
T10544649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Delphine Lasalle |
E248783
|
entity |
| Predicate | relationshipTypeWithLorraineBroughton |
P94605
|
FINISHED |
| Object | romantic and sexual relationship |
—
|
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: romantic and sexual relationship | Statement: [Delphine Lasalle, relationshipTypeWithLorraineBroughton, romantic and sexual relationship]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithLorraineBroughton Context triple: [Delphine Lasalle, relationshipTypeWithLorraineBroughton, romantic and sexual relationship]
-
A.
relationshipToLaurie
Indicates the specific type of relationship or connection that an entity has to Laurie.
-
B.
relationshipTypeWithLizzieEustace
Indicates the specific nature or category of relationship that an entity has with Lizzie Eustace.
-
C.
relationshipToLaureyWilliams
Indicates the nature or type of relational connection an entity has specifically to Laurey Williams.
-
D.
relationshipTypeWithLily Owens
Indicates the specific nature or category of relational connection that an entity has with Lily Owens.
-
E.
relationshipTypeWith Larry Darrell
Indicates the specific type or nature of the relationship that an entity has with Larry Darrell.
- 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_69d381c733c08190ab1dd6239f5f34ae |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d52710869c81909b6db1a190825bad |
completed | April 7, 2026, 3:47 p.m. |
| PD | Predicate disambiguation | batch_69d518fa0b4081909bffc936d78bd77b |
completed | April 7, 2026, 2:47 p.m. |
| PDg | Predicate description generation | batch_69d5270eca0481908573b698390c5b08 |
completed | April 7, 2026, 3:47 p.m. |
Created at: April 6, 2026, 12:33 p.m.