Triple
T32129357
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elliot Stabler – Christopher Meloni |
E820597
|
entity |
| Predicate | primaryProfessionContext |
P167348
|
FINISHED |
| Object | television drama |
—
|
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: television drama | Statement: [Elliot Stabler – Christopher Meloni, primaryProfessionContext, television drama]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryProfessionContext Context triple: [Elliot Stabler – Christopher Meloni, primaryProfessionContext, television drama]
-
A.
leftProfession
Indicates that an entity has stopped or abandoned a particular profession or occupation they previously held.
-
B.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
C.
occupationSetting
chosen
Indicates the typical environment or context in which an occupation is performed.
-
D.
professionalCategory
Indicates the classification of an entity according to its professional field, role, or occupational domain.
-
E.
professionDeterminedBy
Indicates that an entity’s profession is defined or decided based on another factor, condition, or entity.
- 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_69f34902d42c819083a8e6bba9a8bb9a |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a037c894b488190bcbec2eccaff4a01 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379eaa540819095a1c5d9f3513f9b |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 12:29 a.m.