Triple
T37051939
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Digital Visual Interface |
E917071
|
entity |
| Predicate | pinCountVariant |
P187278
|
FINISHED |
| Object | 18+1 pins (single-link DVI-D) |
—
|
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: 18+1 pins (single-link DVI-D) | Statement: [Digital Visual Interface, pinCountVariant, 18+1 pins (single-link DVI-D)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pinCountVariant Context triple: [Digital Visual Interface, pinCountVariant, 18+1 pins (single-link DVI-D)]
-
A.
pinCount
Indicates the number of pins associated with or assigned to a given entity.
-
B.
hasPinCount
Indicates that an entity is associated with a specific number of pins.
-
C.
hasNumberOfPins
Indicates that an entity is associated with a specific count of pins it possesses or uses.
-
D.
dataPinCount
Indicates the number of data pins associated with or used by an entity in a given context.
-
E.
hasPinCountDesignation
Indicates that an entity is assigned a specific designation describing the number of pins it has.
- 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_69f76e94d0308190a3f06890e133c88e |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb34e5576881909394355c8ec6ddd2 |
completed | May 6, 2026, 12:32 p.m. |
| PD | Predicate disambiguation | batch_69fb2f6171e88190bf1e0ee6a644b6a9 |
completed | May 6, 2026, 12:09 p.m. |
| PDg | Predicate description generation | batch_69fb34e4906c8190abb1c293fb84329a |
completed | May 6, 2026, 12:32 p.m. |
Created at: May 3, 2026, 4:14 p.m.