Triple
T19428690
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Atikaya |
E486050
|
entity |
| Predicate | invulnerabilityTo |
P100262
|
FINISHED |
| Object | ordinary weapons |
—
|
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: ordinary weapons | Statement: [Atikaya, invulnerabilityTo, ordinary weapons]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: invulnerabilityTo Context triple: [Atikaya, invulnerabilityTo, ordinary weapons]
-
A.
cannotBeDamagedBy
chosen
Indicates that one entity is immune to harm, injury, or degradation caused by another specified entity or factor.
-
B.
isResistant
Indicates that an entity can withstand, oppose, or is not significantly affected by a specified force, influence, or agent.
-
C.
hasWeakness
Indicates that one entity is vulnerable to, or can be adversely affected or defeated by, another entity.
-
D.
hasRoleInImmunity
Indicates that an entity functions in or contributes to an immune-related process or mechanism.
-
E.
protectingPowerFor
Indicates that one entity serves as a protecting power, safeguarding the interests or rights of another entity, typically in a diplomatic or conflict-related context.
- 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_69d8e8d688f881909c85104a62e09d8a |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6321b78d08190b86cef7c60cbb61c |
completed | April 20, 2026, 2:03 p.m. |
| PD | Predicate disambiguation | batch_69e4fd6e806081909053f325ba01ab6b |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 1:37 p.m.