Triple
T37459115
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lightbomb |
E930872
|
entity |
| Predicate | affectedBySpellDamage |
P188347
|
FINISHED |
| Object | false |
—
|
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: false | Statement: [Lightbomb, affectedBySpellDamage, false]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: affectedBySpellDamage Context triple: [Lightbomb, affectedBySpellDamage, false]
-
A.
takesDamageFrom
Indicates that one entity receives or suffers damage as a result of an action, effect, or interaction caused by another entity.
-
B.
affectedByMagicCoat
Indicates that an action or effect directed at an entity is reflected back to its originator due to the influence of a "Magic Coat"-like protective effect.
-
C.
damageAdjusted
Indicates that the amount of damage has been modified from its original value, typically to account for mitigating or amplifying factors.
-
D.
damageEffect
Indicates that one entity causes harm, reduction, or deterioration to another entity or its properties.
-
E.
damageAmount
Indicates the quantity or extent of damage inflicted, suffered, or associated with an entity or event.
- 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_69f76ec1a1148190b0a961f188d621b0 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fba68077788190b311e027435fcf87 |
completed | May 6, 2026, 8:37 p.m. |
| PD | Predicate disambiguation | batch_69fba34c65ac8190b298f0f00d1dcc0e |
completed | May 6, 2026, 8:23 p.m. |
| PDg | Predicate description generation | batch_69fba67f78348190ab160988e4698394 |
completed | May 6, 2026, 8:37 p.m. |
Created at: May 3, 2026, 4:17 p.m.