Triple
T31051388
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Puerto Rican Pepperpot |
E791274
|
entity |
| Predicate | occupationAsPersona |
P173430
|
FINISHED |
| Object | singer |
—
|
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: singer | Statement: [The Puerto Rican Pepperpot, occupationAsPersona, singer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: occupationAsPersona Context triple: [The Puerto Rican Pepperpot, occupationAsPersona, singer]
-
A.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
B.
occupationAspiration
Indicates a person's desired or intended future occupation or career goal.
-
C.
occupationDuringAlias
Indicates that an entity held a particular occupation specifically during the time period when it was known by a given alias.
-
D.
fictionalOccupation
Indicates that one entity is the imaginary or narrative-based job, role, or profession attributed to another entity within a fictional context.
-
E.
occupationSetting
Indicates the typical environment or context in which an occupation is performed.
- 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_69f224cb08908190ba71ad9aa87518ed |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f6b56ed31481908c3e5d749e46bad9 |
completed | May 3, 2026, 2:39 a.m. |
| PD | Predicate disambiguation | batch_69f6b3a7bdb481908d16a32f49e38c2c |
completed | May 3, 2026, 2:32 a.m. |
| PDg | Predicate description generation | batch_69f6b49339048190b617a6749f648825 |
completed | May 3, 2026, 2:36 a.m. |
Created at: April 29, 2026, 9 p.m.