Triple
T30342224
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kid Glove Killer (1942 film) |
E771786
|
entity |
| Predicate | featuresForensicScience |
P103061
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Kid Glove Killer (1942 film), featuresForensicScience, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresForensicScience Context triple: [Kid Glove Killer (1942 film), featuresForensicScience, true]
-
A.
featuresPrivateDetective
Indicates that the subject includes or involves a private detective as a notable element or character.
-
B.
hasForensicElement
chosen
Indicates that something includes, involves, or is characterized by a forensic component, aspect, or feature.
-
C.
featuresMurderInvestigation
Indicates that the subject involves or includes a murder investigation as a central element or storyline.
-
D.
usedForensicScienceInFiction
Indicates that forensic science is employed as a plot element or investigative method within a fictional work.
-
E.
featuresInvestigationOf
Indicates that something includes or presents an examination, inquiry, or analysis focused on a particular subject or issue.
- 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_69f2248b9a208190bc3e6804acd5afd6 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69ffe8ebe9fc8190b1934a3c4074370c |
completed | May 10, 2026, 2:09 a.m. |
| PD | Predicate disambiguation | batch_69ffe83a5de88190bed3e8bac86e8760 |
completed | May 10, 2026, 2:06 a.m. |
Created at: April 29, 2026, 7:55 p.m.