Triple
T7953474
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frankie Yale |
E184671
|
entity |
| Predicate | roleInCareerOfAlCapone |
P79999
|
FINISHED |
| Object | early employer |
—
|
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: early employer | Statement: [Frankie Yale, roleInCareerOfAlCapone, early employer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInCareerOfAlCapone Context triple: [Frankie Yale, roleInCareerOfAlCapone, early employer]
-
A.
roleInCrime
Indicates the specific function, responsibility, or participation an entity has within the commission of a particular crime.
-
B.
roleInFranchiseHistory
Indicates the specific function, position, or contribution an entity has within the historical development or timeline of a franchise.
-
C.
roleInCompanyHistory
Indicates that an entity held a specific role or position during a particular period or event in a company's history.
-
D.
prisonRole
Indicates a role or function that an entity holds within the context or system of a prison.
-
E.
characterFormerOccupation
Indicates that a character previously held a specific occupation but no longer does.
- 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_69ca8292cba881908a64427b938dac47 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3b5e51c88190abcc0534723e3660 |
completed | March 31, 2026, 3:11 a.m. |
| PD | Predicate disambiguation | batch_69cb0473d7dc8190a25d0cf460b9fcbe |
completed | March 30, 2026, 11:17 p.m. |
| PDg | Predicate description generation | batch_69cb14bbbacc81909c6cf8ec35314bbb |
completed | March 31, 2026, 12:26 a.m. |
Created at: March 30, 2026, 5:10 p.m.