Triple
T25301491
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frank Barone |
E634354
|
entity |
| Predicate | relationshipWithRobertBarone |
P182376
|
FINISHED |
| Object | frequently dismissive and teasing |
—
|
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: frequently dismissive and teasing | Statement: [Frank Barone, relationshipWithRobertBarone, frequently dismissive and teasing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipWithRobertBarone Context triple: [Frank Barone, relationshipWithRobertBarone, frequently dismissive and teasing]
-
A.
relationshipWithRayBarone
Indicates that an entity has some form of personal relationship or connection with Ray Barone.
-
B.
relationshipToFrankDeFazio
Indicates the type or nature of a person's relationship to Frank DeFazio.
-
C.
relationshipWithMarieBarone
Indicates that one entity has a specified interpersonal relationship with the person Marie Barone.
-
D.
hasPoliticalRelationshipWith
Indicates a political connection or association between two entities, such as alliances, rivalries, collaborations, or other forms of political interaction.
-
E.
relationshipWithAnnShankland
Indicates that an entity has some form of personal or professional relationship with Ann Shankland.
- 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_69f78d7211a48190bfb59c406f0bf12f |
completed | May 3, 2026, 6:01 p.m. |
| PD | Predicate disambiguation | batch_69f78b8cb3a881909ebaac1b503988c2 |
completed | May 3, 2026, 5:53 p.m. |
| PDg | Predicate description generation | batch_69f78c6014e08190864785a4fe3e8e73 |
completed | May 3, 2026, 5:56 p.m. |
Created at: April 21, 2026, 1:24 p.m.