Triple
T33505105
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | High on Crack Street |
E858089
|
entity |
| Predicate | featuresOccupationOfSubject |
P2374
|
FINISHED |
| Object | boxer |
—
|
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: boxer | Statement: [High on Crack Street, featuresOccupationOfSubject, boxer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresOccupationOfSubject Context triple: [High on Crack Street, featuresOccupationOfSubject, boxer]
-
A.
subjectOccupation
chosen
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
B.
occupationType
Indicates the specific kind or category of work, profession, or role that an entity performs or holds.
-
C.
occupationSetting
Indicates the typical environment or context in which an occupation is performed.
-
D.
natureOfOccupation
Indicates the type or character of a person's occupation, describing what kind of work or role it is rather than who performs it.
-
E.
derivesFromOccupation
Indicates that one entity originates from, is obtained through, or is a result of another entity’s occupation or professional role.
- 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_69f3497660508190a541826a81f7e9ab |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a01b935e3948190a7af37b07938d618 |
completed | May 11, 2026, 11:10 a.m. |
| PD | Predicate disambiguation | batch_6a01b8d198b48190b01e16ac9ce7e7bd |
completed | May 11, 2026, 11:09 a.m. |
Created at: May 1, 2026, 1:38 a.m.