Triple
T32154073
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Preppy Killer |
E821232
|
entity |
| Predicate | hasSentenceLength |
P199505
|
FINISHED |
| Object | 5 to 15 years in prison |
—
|
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: 5 to 15 years in prison | Statement: [Preppy Killer, hasSentenceLength, 5 to 15 years in prison]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSentenceLength Context triple: [Preppy Killer, hasSentenceLength, 5 to 15 years in prison]
-
A.
sentenceLength
Indicates the length or number of units (such as characters, words, or tokens) that a given sentence contains.
-
B.
hasLineLength
Indicates that one entity has, is characterized by, or is associated with a specific line length value.
-
C.
clauseLength
Indicates the length or number of components in a clause within a larger structure or statement.
-
D.
hasSyllableCount
Indicates that one entity (typically a word or phrase) possesses a specific number of syllables given by the other entity.
-
E.
hasTextLength
Indicates that an entity possesses a textual content whose length (typically in characters or words) is specified or constrained.
- 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_69f34905e098819082191a6922a6d607 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69ff3fb2318c81908a46c2f513608935 |
completed | May 9, 2026, 2:07 p.m. |
| PD | Predicate disambiguation | batch_69ff3e96dcc48190819f6204680d84aa |
completed | May 9, 2026, 2:03 p.m. |
| PDg | Predicate description generation | batch_69ff3fb151008190bf8a90f9f1c5f0c8 |
completed | May 9, 2026, 2:07 p.m. |
Created at: May 1, 2026, 12:32 a.m.