Triple
T38097900
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kent Hull |
E951293
|
entity |
| Predicate | blockingStyle |
P190014
|
FINISHED |
| Object | pass protection |
—
|
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: pass protection | Statement: [Kent Hull, blockingStyle, pass protection]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: blockingStyle Context triple: [Kent Hull, blockingStyle, pass protection]
-
A.
bindingStyle
Indicates the method or format by which two components, systems, or interfaces are connected or integrated for interaction.
-
B.
maskStyle
Indicates the style or design characteristics of a mask used or worn in the described context.
-
C.
closureStyle
Indicates how an item is fastened or secured closed, specifying the type or method of closure used.
-
D.
structuralStyle
Indicates the architectural or design style that characterizes the structure or form of an entity.
-
E.
boardingStyle
Indicates the manner or method by which an entity boards or is boarded onto another entity (such as a vehicle, vessel, or platform).
- 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_69f76f04960c8190a83f14ae4c67f5bc |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fc4748843c8190931432653be4890c |
completed | May 7, 2026, 8:03 a.m. |
| PD | Predicate disambiguation | batch_69fc45646ce481908caf292ff9f06e15 |
completed | May 7, 2026, 7:55 a.m. |
| PDg | Predicate description generation | batch_69fc4747b06c8190a3ea5331f02eedad |
completed | May 7, 2026, 8:03 a.m. |
Created at: May 3, 2026, 4:21 p.m.