Triple
T26147940
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charlie Dattolo |
E659731
|
entity |
| Predicate | relationshipTypeWithMarnie |
P195924
|
FINISHED |
| Object | romantic partner |
—
|
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 partner | Statement: [Charlie Dattolo, relationshipTypeWithMarnie, romantic partner]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithMarnie Context triple: [Charlie Dattolo, relationshipTypeWithMarnie, romantic partner]
-
A.
relationshipStatusWithMarnie
Indicates the nature or current state of the relationship that an entity has with Marnie.
-
B.
relationshipToMariane
Indicates the specific type of relationship or connection that an entity has to Mariane.
-
C.
relationshipToMarcy
Indicates that one entity has a specified personal or social relationship to Marcy.
-
D.
relationshipToMary
Indicates that one entity stands in a specified personal or social relationship to Mary.
-
E.
relationshipTypeWithMaryTyrone
Indicates the specific nature or category of the relationship that an entity has with Mary Tyrone.
- 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_69ee5bc496a88190af7deb7ab5e081de |
completed | April 26, 2026, 6:39 p.m. |
| NER | Named-entity recognition | batch_69fdf5d05cc481909ec9e1b1f0784279 |
completed | May 8, 2026, 2:40 p.m. |
| PD | Predicate disambiguation | batch_69fdf0cdd6948190838864ab3120dfa6 |
completed | May 8, 2026, 2:18 p.m. |
| PDg | Predicate description generation | batch_69fdf5cfa1ec8190b80d887fa1bfb4cf |
completed | May 8, 2026, 2:40 p.m. |
Created at: April 26, 2026, 8:23 p.m.