Triple
T37603874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boogie Tillmon |
E935594
|
entity |
| Predicate | formerProfessionContext |
P102116
|
FINISHED |
| Object | adult entertainment industry |
—
|
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: adult entertainment industry | Statement: [Boogie Tillmon, formerProfessionContext, adult entertainment industry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: formerProfessionContext Context triple: [Boogie Tillmon, formerProfessionContext, adult entertainment industry]
-
A.
characterFormerOccupation
Indicates that a character previously held a specific occupation but no longer does.
-
B.
leftProfession
chosen
Indicates that an entity has stopped or abandoned a particular profession or occupation they previously held.
-
C.
earlierOccupation
Indicates that one occupation held by an entity occurred before another occupation in that entity’s work history.
-
D.
economicRolePast
Indicates that an entity previously held a specific economic function, position, or role in the past.
-
E.
formerSocialStatus
Indicates a relationship where one entity identifies the past or previous social status, rank, or class that another entity once held but no longer does.
- 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_69f76ed0a85481909254a8a89090c826 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a00dc330b148190aaae2ac6a5327960 |
completed | May 10, 2026, 7:27 p.m. |
| PD | Predicate disambiguation | batch_6a00d9d2904881909dafbfe7b9e5ad81 |
completed | May 10, 2026, 7:17 p.m. |
Created at: May 3, 2026, 4:18 p.m.