Triple
T3358418
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | M198 howitzer |
E70658
|
entity |
| Predicate | carriageType |
P47469
|
FINISHED |
| Object | split trail |
—
|
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: split trail | Statement: [M198 howitzer, carriageType, split trail]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: carriageType Context triple: [M198 howitzer, carriageType, split trail]
-
A.
railwayCarriageUsedFor
Indicates that a railway carriage is employed or designated for a particular purpose, function, or type of use.
-
B.
vehicleType
Indicates the specific kind or category of vehicle associated with an entity (e.g., car, bus, bicycle).
-
C.
transportType
Indicates the mode or means of transportation used in carrying something or someone from one place to another.
-
D.
rollingStockType
Indicates the specific category or type of railway rolling stock associated with an entity (e.g., locomotive, passenger car, freight wagon).
-
E.
primaryTransportModel
Indicates that one transport model is designated as the main or default model used for a given context or entity.
- 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_69ad85a660c48190998489309a3b4869 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb26523cc819091006fde7beb32e4 |
completed | March 8, 2026, 5:31 p.m. |
| PD | Predicate disambiguation | batch_69ada42fbe7c8190b9f185b5ab985f17 |
completed | March 8, 2026, 4:30 p.m. |
| PDg | Predicate description generation | batch_69ada52716ec81908e89688a81039394 |
completed | March 8, 2026, 4:34 p.m. |
Created at: March 8, 2026, 3:13 p.m.