Triple
T33446374
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Omar Suarez |
E856515
|
entity |
| Predicate | relationshipToTonyMontana |
P86783
|
FINISHED |
| Object | business associate |
—
|
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: business associate | Statement: [Omar Suarez, relationshipToTonyMontana, business associate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToTonyMontana Context triple: [Omar Suarez, relationshipToTonyMontana, business associate]
-
A.
relationshipToTony
chosen
Indicates the specific type of relationship or connection that an entity has with Tony.
-
B.
relationshipToTina
Indicates the specific type of personal or social relationship that an entity has with Tina.
-
C.
relationshipToTonyaHarding
Indicates the specific personal, professional, or familial relationship that one entity has to Tonya Harding.
-
D.
relationshipToJoeBuck
Indicates the specific familial, social, or professional relationship that one entity has to the person Joe Buck.
-
E.
relationshipToTinaBordereau
Indicates the specific type of personal or professional relationship an entity has with Tina Bordereau.
- 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_69f34971b75881908be360bb041f003c |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a037c8ae0248190b7e2ce4bf852c22d |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379f505c88190ac0879ab422c3054 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 1:37 a.m.