Triple
T9623819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vickers Medium Mark II |
E232407
|
entity |
| Predicate | powerToWeightRatio |
P89318
|
FINISHED |
| Object | about 7.5 hp/tonne |
—
|
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: about 7.5 hp/tonne | Statement: [Vickers Medium Mark II, powerToWeightRatio, about 7.5 hp/tonne]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: powerToWeightRatio Context triple: [Vickers Medium Mark II, powerToWeightRatio, about 7.5 hp/tonne]
-
A.
vehiclePower
Indicates the amount or type of power a vehicle can produce or is rated to deliver.
-
B.
enginePower
Indicates the power output produced by an engine, typically quantifying its capability to perform work or generate mechanical energy.
-
C.
powerRange
Indicates the range of power values within which an entity operates, applies, or is considered valid.
-
D.
winnerPowertrainType
Indicates the type of powertrain used by the entity that is identified as the winner in a given context or competition.
-
E.
drivingForce
Indicates a causal influence or motivating factor that propels or significantly shapes another process, event, or outcome.
- 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_69ca848793ec8190a93a12383a754dc0 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9ad76b148190a38fadee06594db4 |
completed | April 1, 2026, 10:23 p.m. |
| PD | Predicate disambiguation | batch_69ccd5aa1d2c8190a287bf1cf4a3037e |
completed | April 1, 2026, 8:22 a.m. |
| PDg | Predicate description generation | batch_69ccd93fc45c8190a823305e461e581d |
completed | April 1, 2026, 8:37 a.m. |
Created at: March 30, 2026, 8:10 p.m.