Triple
T32898746
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laszlo Kreizler |
E841546
|
entity |
| Predicate | influencesInStory |
P188452
|
FINISHED |
| Object | development of modern criminal profiling |
—
|
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: development of modern criminal profiling | Statement: [Laszlo Kreizler, influencesInStory, development of modern criminal profiling]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: influencesInStory Context triple: [Laszlo Kreizler, influencesInStory, development of modern criminal profiling]
-
A.
influencesPlotOf
Indicates that one entity has an effect on or helps shape the storyline or narrative development of another entity.
-
B.
influencesThrough
Indicates that one entity affects or alters another entity indirectly by means of an intermediate factor, channel, or mechanism.
-
C.
influenceOf
Indicates that one entity affects, shapes, or alters the state, behavior, or properties of another entity.
-
D.
influencedIn
Indicates that one entity had an effect on or shaped another entity within a specific context, domain, or setting.
-
E.
incorporatesInfluence
Indicates that one entity integrates or absorbs the influence, ideas, or characteristics of another into itself.
- 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_69f34945ae408190b72d8118c83beb77 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fba78aca4c8190b8f1831e8cc04e06 |
completed | May 6, 2026, 8:41 p.m. |
| PD | Predicate disambiguation | batch_69fba34a65a4819088bac6c17542d71c |
completed | May 6, 2026, 8:23 p.m. |
| PDg | Predicate description generation | batch_69fba789c1188190973a919bfe2871f3 |
completed | May 6, 2026, 8:41 p.m. |
Created at: May 1, 2026, 1:19 a.m.