Triple
T8832170
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Penning trap |
E210170
|
entity |
| Predicate | usedAtFacilityType |
P84903
|
FINISHED |
| Object | nuclear research facilities |
—
|
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: nuclear research facilities | Statement: [Penning trap, usedAtFacilityType, nuclear research facilities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedAtFacilityType Context triple: [Penning trap, usedAtFacilityType, nuclear research facilities]
-
A.
usedFacilityName
Indicates that an entity made use of a facility identified by a particular name.
-
B.
hasFacilityType
Indicates that an entity possesses or is associated with a specific type or category of facility.
-
C.
basedOnFacility
Indicates that something is determined, derived, or decided according to the characteristics, rules, or conditions of a particular facility.
-
D.
designedFacilityType
Indicates the type or category of facility that something (such as a plan, system, or component) is specifically designed for.
-
E.
hasHospitalType
Indicates that a hospital is classified as belonging to a specific type or category (e.g., general, specialized, teaching).
- 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_69ca8365b28081909e48e45e95dfc405 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc605005788190a4df1fe317f3056a |
completed | April 1, 2026, 12:01 a.m. |
| PD | Predicate disambiguation | batch_69cc5c23d08481908d8c9b0ad3d1dc00 |
completed | March 31, 2026, 11:43 p.m. |
| PDg | Predicate description generation | batch_69cc5cff3608819081d2d7e5c16d44b7 |
completed | March 31, 2026, 11:47 p.m. |
Created at: March 30, 2026, 6:47 p.m.