Triple
T29340679
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hector LeMans |
E744027
|
entity |
| Predicate | schemeType |
P166656
|
FINISHED |
| Object | Department of Death ticket fraud |
—
|
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: Department of Death ticket fraud | Statement: [Hector LeMans, schemeType, Department of Death ticket fraud]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: schemeType Context triple: [Hector LeMans, schemeType, Department of Death ticket fraud]
-
A.
specType
Indicates the specific type or category of a specification that an entity is associated with.
-
B.
signatureType
Indicates the specific kind or category of a signature associated with an entity or action.
-
C.
theoryType
Indicates the classification relationship that specifies what type or category of theory an entity belongs to.
-
D.
simType
Indicates that two entities share the same or a comparable type, category, or classification.
-
E.
symbolType
Indicates the classification or category of a symbol based on its role, form, or function within a given system.
- 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_69f09126cfcc8190899b16fbf3c2bf7b |
completed | April 28, 2026, 10:51 a.m. |
| NER | Named-entity recognition | batch_69f66926e7a48190a1b580fd9fe67c31 |
completed | May 2, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69f660f4f7a88190b93c60d76b86c912 |
completed | May 2, 2026, 8:39 p.m. |
| PDg | Predicate description generation | batch_69f661b47d088190934f63884a203261 |
completed | May 2, 2026, 8:42 p.m. |
Created at: April 28, 2026, 1:33 p.m.