Triple
T5292799
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Loves Music, Loves to Dance |
E119779
|
entity |
| Predicate | includesVictimProfile |
P63355
|
FINISHED |
| Object | single women responding to personal ads |
—
|
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: single women responding to personal ads | Statement: [Loves Music, Loves to Dance, includesVictimProfile, single women responding to personal ads]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesVictimProfile Context triple: [Loves Music, Loves to Dance, includesVictimProfile, single women responding to personal ads]
-
A.
portraysAsVictim
Indicates that one entity represents or depicts another entity as a victim in a given context or narrative.
-
B.
coVictim
Indicates that two or more entities are victims in the same harmful event or incident.
-
C.
isVictimOf
Indicates that one entity suffers harm, loss, or wrongdoing as a result of another entity’s actions or events.
-
D.
legalStatusOfVictims
Indicates the legal classification or condition of the victims in relation to the event or action described.
-
E.
victimGroup
Indicates that one group or entity is the target or recipient of harm, abuse, or wrongdoing caused by another.
- 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_69bd446f22b88190b6a47fb91c68a3e7 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd8682d18c8190bbb35cc75c8a7c12 |
completed | March 20, 2026, 5:40 p.m. |
| PD | Predicate disambiguation | batch_69bd844dfdac819086efedd1cbebff84 |
completed | March 20, 2026, 5:30 p.m. |
| PDg | Predicate description generation | batch_69bd86800630819096dad2eb2248c372 |
completed | March 20, 2026, 5:40 p.m. |
Created at: March 20, 2026, 1:52 p.m.