Triple
T38404171
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PCMCIA Type II |
E900976
|
entity |
| Predicate | thinnerThan |
P190203
|
FINISHED |
| Object | PCMCIA Type III |
—
|
NE NERFINISHED |
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: PCMCIA Type III | Statement: [PCMCIA Type II, thinnerThan, PCMCIA Type III]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: thinnerThan Context triple: [PCMCIA Type II, thinnerThan, PCMCIA Type III]
-
A.
isThinline
Indicates that one entity is characterized by having a thin, narrow, or slender form or profile relative to a relevant standard or comparison.
-
B.
smallerThan
Indicates that one entity has a strictly lesser size, dimension, or magnitude than another entity.
-
C.
narrowerThan
chosen
Indicates that one entity has a smaller width or breadth than another entity.
-
D.
hasThicknessRange
Indicates that an entity is associated with a minimum and maximum thickness value defining the range of its thickness.
-
E.
thickness
Indicates the measure of how deep or wide an object or layer is from one surface or side to its opposite.
- 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_69f76e6071a081909eea7a670d21420c |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fcd1499e2c81909bafd84dc4810f45 |
completed | May 7, 2026, 5:52 p.m. |
| PD | Predicate disambiguation | batch_69fcccf024ec819086383ffbb6cfc036 |
completed | May 7, 2026, 5:33 p.m. |
Created at: May 3, 2026, 4:31 p.m.