Triple
T36958276
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Heavy Weapon Dude |
E914242
|
entity |
| Predicate | damageCharacteristic |
P81550
|
FINISHED |
| Object | high damage per second |
—
|
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 damage per second | Statement: [Heavy Weapon Dude, damageCharacteristic, high damage per second]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: damageCharacteristic Context triple: [Heavy Weapon Dude, damageCharacteristic, high damage per second]
-
A.
damageDescription
chosen
Indicates a textual description of the nature, extent, or characteristics of damage associated with an entity or event.
-
B.
damageRating
Indicates the assessed level or severity of damage associated with an entity or event.
-
C.
damageEffect
Indicates that one entity causes harm, reduction, or deterioration to another entity or its properties.
-
D.
damageClass
Indicates the type or category of damage associated with an action, event, or interaction between entities.
-
E.
damageLeadsTo
Indicates that one instance of damage causally results in or contributes to another specified outcome or condition.
- 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_69f76e8c498c8190b2842db80aea8b3b |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb34e5576881909394355c8ec6ddd2 |
completed | May 6, 2026, 12:32 p.m. |
| PD | Predicate disambiguation | batch_69fb2f6171e88190bf1e0ee6a644b6a9 |
completed | May 6, 2026, 12:09 p.m. |
Created at: May 3, 2026, 4:13 p.m.