Triple
T38641154
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TSMC N7P |
E938596
|
entity |
| Predicate | maskCount |
P173850
|
FINISHED |
| Object | similar mask count to TSMC N7 |
—
|
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: similar mask count to TSMC N7 | Statement: [TSMC N7P, maskCount, similar mask count to TSMC N7]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maskCount Context triple: [TSMC N7P, maskCount, similar mask count to TSMC N7]
-
A.
maskCountApproximate
chosen
Indicates that the number of masks involved is represented as an approximate (non-exact) count.
-
B.
maskStatus
Indicates whether an entity is wearing a mask, not wearing one, or has an unspecified mask-wearing state.
-
C.
mask
Indicates that one entity covers, conceals, or obscures another entity, typically to hide its identity, appearance, or specific features.
-
D.
maskColor
Indicates the color attribute associated with a mask.
-
E.
maskOrGimmick
Indicates that one entity serves as a mask, disguise, or gimmick used by another entity, typically to conceal identity or create a particular impression.
- 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_69f76ed948ec81908ce7811608a8f359 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fcdb0de8c08190928cd1323f80ab5c |
completed | May 7, 2026, 6:33 p.m. |
| PD | Predicate disambiguation | batch_69fcd9017dd88190b32a73fe78909740 |
completed | May 7, 2026, 6:25 p.m. |
Created at: May 3, 2026, 4:32 p.m.