Triple
T7953252
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nick Gordon |
E184665
|
entity |
| Predicate | foundLiableInCivilCourt |
P4329
|
FINISHED |
| Object | wrongful death of Bobbi Kristina Brown |
—
|
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: wrongful death of Bobbi Kristina Brown | Statement: [Nick Gordon, foundLiableInCivilCourt, wrongful death of Bobbi Kristina Brown]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: foundLiableInCivilCourt Context triple: [Nick Gordon, foundLiableInCivilCourt, wrongful death of Bobbi Kristina Brown]
-
A.
establishesLiabilityFor
chosen
Indicates that one party is held legally responsible or accountable for a particular act, omission, or outcome.
-
B.
haveCivilLaw
Indicates that an entity is subject to, governed by, or operates under a civil law legal system.
-
C.
isWithinCivilJurisdiction
Indicates that one entity falls under the legal authority, control, or governance of a specified civil jurisdiction.
-
D.
affectedCourt
Indicates that a particular court is impacted or influenced by a specified action, decision, or legal matter.
-
E.
usedInCourts
Indicates that something is employed or applied within legal court settings, such as in judicial proceedings or courtroom processes.
- 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_69ca8292cba881908a64427b938dac47 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3b5e51c88190abcc0534723e3660 |
completed | March 31, 2026, 3:11 a.m. |
| PD | Predicate disambiguation | batch_69cb0473d7dc8190a25d0cf460b9fcbe |
completed | March 30, 2026, 11:17 p.m. |
Created at: March 30, 2026, 5:10 p.m.