Triple
T21748149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Missouri Law Enforcement Memorial |
E536839
|
entity |
| Predicate | honorsProfession |
P15675
|
FINISHED |
| Object | law enforcement officers |
—
|
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: law enforcement officers | Statement: [Missouri Law Enforcement Memorial, honorsProfession, law enforcement officers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: honorsProfession Context triple: [Missouri Law Enforcement Memorial, honorsProfession, law enforcement officers]
-
A.
honors
Indicates that one entity shows respect, recognition, or esteem toward another entity, often in a formal or ceremonial way.
-
B.
honorsRole
Indicates that one entity formally recognizes and respects the position, title, or role held by another entity.
-
C.
honorificDegree
Indicates that an entity has been awarded an honorary academic degree or title, typically in recognition of merit rather than completion of formal study.
-
D.
professionAttribute
Indicates that a specific attribute, quality, or characteristic is associated with a given profession.
-
E.
honoredThrough
chosen
Indicates that one entity is recognized or commemorated by means of another entity, event, 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_69e0c46eab808190b848242d63a17c47 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f01a77e19c81909bf26f96aa41a7ce |
completed | April 28, 2026, 2:24 a.m. |
| PD | Predicate disambiguation | batch_69e6969c16fc8190b5126c169317d85d |
completed | April 20, 2026, 9:11 p.m. |
Created at: April 16, 2026, 6:50 p.m.