Triple
T761998
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trinity test device |
E16090
|
entity |
| Predicate | explosiveLensType |
P18830
|
FINISHED |
| Object | high-explosive lenses |
—
|
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: high-explosive lenses | Statement: [Trinity test device, explosiveLensType, high-explosive lenses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: explosiveLensType Context triple: [Trinity test device, explosiveLensType, high-explosive lenses]
-
A.
numberOfExplosiveLenses
Indicates the quantity of explosive lenses associated with or used in a given object, system, or configuration.
-
B.
ammunitionType
Indicates the specific kind or category of ammunition associated with or used by an entity.
-
C.
numberOfExplosions
Indicates the count of distinct explosion events associated with an entity or situation.
-
D.
gunType
Indicates the specific category or kind of gun associated with an entity.
-
E.
bombLoad
Indicates the amount or configuration of bombs carried by an entity, typically an aircraft, for a mission or operation.
- 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_69a493684ee48190bd43b7c78da4aec8 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a6841f388190a6d08c3bf5c17fe4 |
completed | March 1, 2026, 8:50 p.m. |
| PD | Predicate disambiguation | batch_69a4a5048a8081908d0542214142664a |
completed | March 1, 2026, 8:43 p.m. |
| PDg | Predicate description generation | batch_69a4a58c0a84819094f07658dc651b36 |
completed | March 1, 2026, 8:46 p.m. |
Created at: March 1, 2026, 7:37 p.m.