Triple
T4856209
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ares armoured personnel carrier |
E108541
|
entity |
| Predicate | hasCrewCompartment |
P30403
|
FINISHED |
| Object | armoured troop compartment |
—
|
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: armoured troop compartment | Statement: [Ares armoured personnel carrier, hasCrewCompartment, armoured troop compartment]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCrewCompartment Context triple: [Ares armoured personnel carrier, hasCrewCompartment, armoured troop compartment]
-
A.
hasCrewCompartmentMaterial
Indicates that an entity’s crew compartment is made of, or incorporates, a specified material.
-
B.
hasCrewCapacity
Indicates that an entity is capable of accommodating a specified number of crew members.
-
C.
carriesCrewOf
Indicates that one entity serves as the vehicle or vessel that transports the crew belonging to another entity.
-
D.
hasCargoDoorVariant
Indicates that one entity is a specific cargo-door-equipped version or configuration variant of another entity.
-
E.
hasPassengerArea
chosen
Indicates that an object or vehicle includes a designated area intended for carrying passengers.
- 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_69bd440a89548190a5f14ba6da6b97dc |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6ddd17d881909f7731ff2b460e83 |
completed | March 20, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69bd6c2557388190a2d15571bacd24f3 |
completed | March 20, 2026, 3:47 p.m. |
Created at: March 20, 2026, 1:26 p.m.