Triple
T20879212
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Public Enemy No. 1 (FBI label for John Dillinger) |
E514098
|
entity |
| Predicate | appliedToPersonGender |
P122275
|
FINISHED |
| Object | male |
—
|
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: male | Statement: [Public Enemy No. 1 (FBI label for John Dillinger), appliedToPersonGender, male]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appliedToPersonGender Context triple: [Public Enemy No. 1 (FBI label for John Dillinger), appliedToPersonGender, male]
-
A.
appliesToPerson
Indicates that something (such as a rule, condition, or attribute) is relevant or applicable to a specific person.
-
B.
appliesToPersonType
Indicates that something (such as a rule, condition, or attribute) is relevant or applicable specifically to a certain type or category of person.
-
C.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
-
D.
refersToGenderOfPerson
chosen
Indicates that something specifies, denotes, or is associated with the gender of a particular person.
-
E.
usedByGender
Indicates that something is utilized, applied, or engaged in by entities of a specified gender.
- 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_69e0b4f733f081908a401c0b7beb0b9f |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c678b394819096a17de9e04cd74f |
completed | April 21, 2026, 12:36 a.m. |
| PD | Predicate disambiguation | batch_69e5c9a8dc148190b33ff51894e2a8f9 |
completed | April 20, 2026, 6:37 a.m. |
Created at: April 16, 2026, 12:45 p.m.