Triple
T29654975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Immortal |
E750239
|
entity |
| Predicate | damageBonus |
P167558
|
FINISHED |
| Object | +damage vs armored |
—
|
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: +damage vs armored | Statement: [Immortal, damageBonus, +damage vs armored]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: damageBonus Context triple: [Immortal, damageBonus, +damage vs armored]
-
A.
damageScaling
Indicates how the amount of damage changes in proportion to certain factors, such as level, stats, or conditions.
-
B.
damageBasis
Indicates the underlying reason, cause, or basis on which damage is determined or assessed in a given context.
-
C.
damageAdjusted
Indicates that the amount of damage has been modified from its original value, typically to account for mitigating or amplifying factors.
-
D.
damageRating
Indicates the assessed level or severity of damage associated with an entity or event.
-
E.
damageEffect
Indicates that one entity causes harm, reduction, or deterioration to another entity or its properties.
- 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_69f0d6226fe881908819197c9ef9ee04 |
completed | April 28, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69f66f274ba08190bcdbdfeccf4af09d |
completed | May 2, 2026, 9:39 p.m. |
| PD | Predicate disambiguation | batch_69f6659f246081909821c5f452d14e8f |
completed | May 2, 2026, 8:59 p.m. |
| PDg | Predicate description generation | batch_69f6691da93081909deaf680614fc900 |
completed | May 2, 2026, 9:14 p.m. |
Created at: April 28, 2026, 6:54 p.m.