Triple
T38404170
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PCMCIA Type II |
E900976
|
entity |
| Predicate | thickerThan |
P190848
|
FINISHED |
| Object | PCMCIA Type I |
—
|
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 I | Statement: [PCMCIA Type II, thickerThan, PCMCIA Type I]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: thickerThan Context triple: [PCMCIA Type II, thickerThan, PCMCIA Type I]
-
A.
thickness
Indicates the measure of how deep or wide an object or layer is from one surface or side to its opposite.
-
B.
thickestIn
Indicates that one entity has the greatest thickness among a specified set or within a given context.
-
C.
hasThicknessRange
Indicates that an entity is associated with a minimum and maximum thickness value defining the range of its thickness.
-
D.
isThickenedWith
Indicates that one substance has been made more viscous or dense by adding another substance that serves as a thickening agent.
-
E.
hasApproximateAverageThickness
Indicates that an entity possesses a thickness value that is an estimated or typical average rather than an exact measurement.
- 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_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. |
| PDg | Predicate description generation | batch_69fcd148e6d4819082c118832ecc599b |
completed | May 7, 2026, 5:52 p.m. |
Created at: May 3, 2026, 4:31 p.m.