Triple
T16095812
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lt. Joe Bookman |
E390479
|
entity |
| Predicate | treatsCaseAs |
P121881
|
FINISHED |
| Object | serious criminal investigation |
—
|
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: serious criminal investigation | Statement: [Lt. Joe Bookman, treatsCaseAs, serious criminal investigation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: treatsCaseAs Context triple: [Lt. Joe Bookman, treatsCaseAs, serious criminal investigation]
-
A.
treatsRightAs
Indicates that one entity provides medical or therapeutic treatment to another entity who is identified as the right-hand participant in the relationship.
-
B.
letterCase
Indicates the relationship between a character or string and its typographical case (such as uppercase, lowercase, or mixed case).
-
C.
caseSensitivityVariant
Indicates that one string or textual form is a variant of another that differs only in letter casing (e.g., uppercase vs lowercase).
-
D.
treatsLightAs
Indicates that an entity regards or handles light in a particular way or manner.
-
E.
caseInfluenced
Indicates that one legal case has affected, shaped, or contributed to the outcome, reasoning, or development of another case.
- 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_69d87f198bc48190a8b7e53ca15b7ead |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e1ff63edb0819092cbb671967bbdcd |
completed | April 17, 2026, 9:37 a.m. |
| PD | Predicate disambiguation | batch_69e182804208819087f35307cd6e4103 |
completed | April 17, 2026, 12:44 a.m. |
| PDg | Predicate description generation | batch_69e1ff5cd7e481908a29214139a3de2e |
completed | April 17, 2026, 9:37 a.m. |
Created at: April 10, 2026, 4:59 a.m.