Triple
T27343297
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Super Lightweight Tank |
E684166
|
entity |
| Predicate | weightReductionMethod |
P138704
|
FINISHED |
| Object | use of aluminum-lithium alloys |
—
|
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: use of aluminum-lithium alloys | Statement: [Super Lightweight Tank, weightReductionMethod, use of aluminum-lithium alloys]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: weightReductionMethod Context triple: [Super Lightweight Tank, weightReductionMethod, use of aluminum-lithium alloys]
-
A.
reduction
Indicates a relationship where something is decreased in amount, size, intensity, or degree compared to a prior state or reference.
-
B.
weightSavingFeatures
chosen
Indicates that something includes design elements or characteristics specifically intended to reduce its weight compared to a standard or baseline version.
-
C.
cutDown
Indicates that an agent causes something standing or elevated (such as a tree or structure) to fall or be reduced by cutting.
-
D.
reduces
Indicates that one entity causes a decrease in the amount, intensity, degree, or impact of another entity.
-
E.
وزن
Indicates a relationship where one entity has, measures, or is characterized by a certain weight or mass.
- 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_69ef1480a76481908684256ddd5bfda3 |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69f661b58ac48190907b6c6e9ccc2c59 |
completed | May 2, 2026, 8:42 p.m. |
| PD | Predicate disambiguation | batch_69f660eea4648190b0d5e24293607813 |
completed | May 2, 2026, 8:39 p.m. |
Created at: April 27, 2026, 11:44 a.m.