Triple
T37012535
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Combat Shotgun |
E915986
|
entity |
| Predicate | hasDamageProfile |
P123825
|
FINISHED |
| Object | high burst damage at close range |
—
|
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: high burst damage at close range | Statement: [Combat Shotgun, hasDamageProfile, high burst damage at close range]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDamageProfile Context triple: [Combat Shotgun, hasDamageProfile, high burst damage at close range]
-
A.
hasTypeOfDamage
Indicates that an entity experiences or exhibits a specific kind or category of damage.
-
B.
hasAttackProfile
chosen
Indicates that an entity is associated with a specific pattern, method, or characteristics of attack it can perform or employ.
-
C.
hasDam
Indicates that a watercourse, reservoir, or similar feature is impounded or controlled by a specific dam.
-
D.
hasDamCount
Indicates the number of dams associated with or present on a given entity (such as a river, region, or site).
-
E.
damageTo
Indicates a relationship where one entity causes harm, loss, or deterioration to another entity.
- 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_69f76e90ed548190b187d2475f5c807d |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb154c0fe08190a2e41e7a29b6055f |
completed | May 6, 2026, 10:17 a.m. |
| PD | Predicate disambiguation | batch_69f9fecc005c8190be082a8689193745 |
completed | May 5, 2026, 2:29 p.m. |
Created at: May 3, 2026, 4:14 p.m.