Triple
T25301489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frank Barone |
E634354
|
entity |
| Predicate | relationshipWithMarieBarone |
P181719
|
FINISHED |
| Object | often argumentative but enduring marriage |
—
|
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: often argumentative but enduring marriage | Statement: [Frank Barone, relationshipWithMarieBarone, often argumentative but enduring marriage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipWithMarieBarone Context triple: [Frank Barone, relationshipWithMarieBarone, often argumentative but enduring marriage]
-
A.
relationshipToMariane
Indicates the specific type of relationship or connection that an entity has to Mariane.
-
B.
relationshipToTinaBordereau
Indicates the specific type of personal or professional relationship an entity has with Tina Bordereau.
-
C.
relationshipWithMarcello
Indicates that there exists some form of relationship or connection between an entity and Marcello.
-
D.
relationshipWithBarbaraSopranoGiglione
Indicates that one entity has a specified personal or familial relationship with Barbara Soprano Giglione.
-
E.
relationshipToNicole
Indicates the specific type of relationship or connection that an entity has with Nicole.
- 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_69e75a972c6481909bc11710e8d30a6c |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f7824dc3f0819092a5102895b4a478 |
completed | May 3, 2026, 5:13 p.m. |
| PD | Predicate disambiguation | batch_69f780fc5ed88190b7200ee5a29940af |
completed | May 3, 2026, 5:08 p.m. |
| PDg | Predicate description generation | batch_69f7817c79e081908e685c48165e086b |
completed | May 3, 2026, 5:10 p.m. |
Created at: April 21, 2026, 1:24 p.m.