Triple
T5292796
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Loves Music, Loves to Dance |
E119779
|
entity |
| Predicate | hasMotiveOfKiller |
P34163
|
FINISHED |
| Object | twisted romantic obsession |
—
|
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: twisted romantic obsession | Statement: [Loves Music, Loves to Dance, hasMotiveOfKiller, twisted romantic obsession]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMotiveOfKiller Context triple: [Loves Music, Loves to Dance, hasMotiveOfKiller, twisted romantic obsession]
-
A.
reasonForMurder
chosen
Indicates the motive or underlying cause that led someone to commit a murder.
-
B.
hasMannerOfDeathOfVictim
Indicates the specific way or circumstances in which the victim died in relation to the event or action being described.
-
C.
allegedToHaveKilled
Indicates that one entity is claimed or accused, but not proven, to have killed another entity.
-
D.
hasMannerOfDeath
Indicates the specific way or circumstances in which an entity died, such as natural causes, accident, homicide, or suicide.
-
E.
hasMotiveElement
Indicates that one entity includes, specifies, or is characterized by a particular motive-related component or factor in a broader relationship or action.
- 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_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. |
Created at: March 20, 2026, 1:52 p.m.