Triple
T34920895
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lawrence Jameson |
E1007132
|
entity |
| Predicate | typicalVictim |
P140041
|
FINISHED |
| Object | wealthy women vacationing on the French Riviera |
—
|
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: wealthy women vacationing on the French Riviera | Statement: [Lawrence Jameson, typicalVictim, wealthy women vacationing on the French Riviera]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalVictim Context triple: [Lawrence Jameson, typicalVictim, wealthy women vacationing on the French Riviera]
-
A.
hasTypicalVictimRole
chosen
Indicates that an entity typically occupies the role of a victim in the context of a particular action, event, or relationship.
-
B.
coVictim
Indicates that two or more entities are victims in the same harmful event or incident.
-
C.
threatenedVictim
Indicates that one entity has issued or posed a threat of harm or adverse consequences toward another entity.
-
D.
victimGroup
Indicates that one group or entity is the target or recipient of harm, abuse, or wrongdoing caused by another.
-
E.
portraysAsVictim
Indicates that one entity represents or depicts another entity as a victim in a given context or narrative.
- 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_69f76dc2b6b0819095a61debbd405269 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a037c8c34f88190ace26f555827f23e |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379ff1ba081908eda86acefcf69fb |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4 p.m.