Triple
T8052238
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norman Pitkin |
E187701
|
entity |
| Predicate | occupationInNarrative |
P69186
|
FINISHED |
| Object | milkman |
—
|
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: milkman | Statement: [Norman Pitkin, occupationInNarrative, milkman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: occupationInNarrative Context triple: [Norman Pitkin, occupationInNarrative, milkman]
-
A.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
B.
hasOccupationDuringStory
chosen
Indicates that an entity holds or performs a particular occupation or job role during the time span covered by the story.
-
C.
fictionalOccupation
Indicates that one entity is the imaginary or narrative-based job, role, or profession attributed to another entity within a fictional context.
-
D.
occupationType
Indicates the specific kind or category of work, profession, or role that an entity performs or holds.
-
E.
portraysProfession
Indicates that one entity depicts or represents another entity in a specific profession or occupational 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_69ca82b15e948190a62fd7af5218426a |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3f7aab6481909d4a7cb9eabcd2af |
completed | March 31, 2026, 3:28 a.m. |
| PD | Predicate disambiguation | batch_69cb049a1b9c8190811c396421ebf9c9 |
completed | March 30, 2026, 11:17 p.m. |
Created at: March 30, 2026, 5:24 p.m.