Triple
T8130640
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FV432 |
E189843
|
entity |
| Predicate | armourProtection |
P81637
|
FINISHED |
| Object | small-arms fire |
—
|
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: small-arms fire | Statement: [FV432, armourProtection, small-arms fire]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: armourProtection Context triple: [FV432, armourProtection, small-arms fire]
-
A.
armourThickness
Indicates the measured thickness of an entity’s protective armor in the context of defense or shielding.
-
B.
armour
Indicates that an entity provides protective covering or defense for another entity.
-
C.
armourBelt
Indicates a relationship where an armour belt is equipped on, attached to, or associated with an entity (such as a character, vehicle, or structure) as protective gear.
-
D.
armorType
Indicates the specific category or classification of protective armor associated with an entity.
-
E.
hasTypicalArmor
Indicates that an entity normally wears or is equipped with a standard or characteristic type of armor.
- 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_69ca82bcb4848190a9a9d036ad768642 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb4c4c2e388190b86854f8b1765e61 |
completed | March 31, 2026, 4:23 a.m. |
| PD | Predicate disambiguation | batch_69cb3696379c8190a20965e59ed8f370 |
completed | March 31, 2026, 2:51 a.m. |
| PDg | Predicate description generation | batch_69cb4c496a8c81909aea840248d85d50 |
completed | March 31, 2026, 4:23 a.m. |
Created at: March 30, 2026, 5:34 p.m.