Triple
T6492341
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Lofaro |
E148070
|
entity |
| Predicate | basedOnProfession |
P71047
|
FINISHED |
| Object | television comedy production |
—
|
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 comedy production | Statement: [Tom Lofaro, basedOnProfession, television comedy production]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: basedOnProfession Context triple: [Tom Lofaro, basedOnProfession, television comedy production]
-
A.
includesProfession
Indicates that one entity’s set of attributes, roles, or members contains a specific profession as part of it.
-
B.
usedByOccupation
Indicates that something (such as a tool, method, or resource) is utilized in the performance of a particular occupation or job.
-
C.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
D.
memberProfession
Indicates that a member or individual holds or practices a particular profession or occupation.
-
E.
isAssociatedWithProfessionOfBearer
Indicates that one entity is connected to, or involved with, the profession or occupational role held by another entity.
- 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_69c009088f3081909cd467b05919de30 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c06a9bf9208190b0957eda06ed3d65 |
completed | March 22, 2026, 10:18 p.m. |
| PD | Predicate disambiguation | batch_69c06740bebc81909d9d6956baa2bcb9 |
completed | March 22, 2026, 10:03 p.m. |
| PDg | Predicate description generation | batch_69c067f1ef148190bc0355abe83f7e16 |
completed | March 22, 2026, 10:06 p.m. |
Created at: March 22, 2026, 4:53 p.m.