Triple
T1196313
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Micromegas detectors |
E25675
|
entity |
| Predicate | hasDriftGapThickness |
P9690
|
FINISHED |
| Object | few millimeters |
—
|
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: few millimeters | Statement: [Micromegas detectors, hasDriftGapThickness, few millimeters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDriftGapThickness Context triple: [Micromegas detectors, hasDriftGapThickness, few millimeters]
-
A.
gapBetween
Indicates the spatial or temporal distance or separation that exists between two entities.
-
B.
thickness
chosen
Indicates the measure of how deep or wide an object or layer is from one surface or side to its opposite.
-
C.
hasCrustalThickness
Indicates the relationship in which an object or region possesses a specified thickness of its crust.
-
D.
depthMetresApprox
Indicates an approximate measurement of how deep something is in metres, rather than an exact value.
-
E.
hasAverageDepth
Indicates that an entity possesses a specified mean depth value, typically measured over its entire extent or area.
- 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_69a49429f5ec8190a6a205eb0ae81e5e |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd7a756c819085d695acfffeaceb |
completed | March 1, 2026, 10:28 p.m. |
| PD | Predicate disambiguation | batch_69a4bb5d40a08190b7682d8ef8075421 |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:46 p.m.