Triple
T16434728
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2007 Knoxville murders of Channon Christian and Christopher Newsom |
E399152
|
entity |
| Predicate | victimGender |
P95608
|
FINISHED |
| Object | female |
—
|
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: female | Statement: [2007 Knoxville murders of Channon Christian and Christopher Newsom, victimGender, female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: victimGender Context triple: [2007 Knoxville murders of Channon Christian and Christopher Newsom, victimGender, female]
-
A.
hasPerpetratorGender
Indicates that an action, event, or offense is associated with the specified gender of the perpetrator.
-
B.
victimGroup
Indicates that one group or entity is the target or recipient of harm, abuse, or wrongdoing caused by another.
-
C.
victimAge
Indicates the age of the person who is the victim in the described event or situation.
-
D.
genderDepicted
chosen
Indicates that the relationship specifies the gender of the entity as it is represented or portrayed in some context.
-
E.
victimRole
Indicates that one entity participates in an event or situation specifically in the role of the victim or harmed party.
- 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_69d87f2c6778819080fcfae53be8f12a |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32ba1023481909588aa6a3c677886 |
completed | April 18, 2026, 6:58 a.m. |
| PD | Predicate disambiguation | batch_69e22701d2288190bf8676050758f172 |
completed | April 17, 2026, 12:26 p.m. |
Created at: April 10, 2026, 5:10 a.m.