Triple
T11658830
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ender Dragon |
E277072
|
entity |
| Predicate | cannotBeDamagedBy |
P100262
|
FINISHED |
| Object | snowballs |
—
|
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: snowballs | Statement: [Ender Dragon, cannotBeDamagedBy, snowballs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cannotBeDamagedBy Context triple: [Ender Dragon, cannotBeDamagedBy, snowballs]
-
A.
canBeDefendedIn
Indicates that something (such as a claim, action, or position) is capable of being justified or supported within a specified context, forum, or framework.
-
B.
coversDamageType
Indicates that one entity provides protection, compensation, or applicability for a specified type of damage.
-
C.
hasBaseDefense
Indicates that an entity possesses a specified level or value of defensive capability in its default or starting state.
-
D.
damageTo
Indicates a relationship where one entity causes harm, loss, or deterioration to another entity.
-
E.
effectivenessAgainst
Indicates how well one entity performs in countering, influencing, or mitigating the impact of another entity.
- 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_69d6aafbb3c081908a9cdb4ecb8d981d |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a3d19c788190826d849a6ffedc72 |
completed | April 10, 2026, 7:16 a.m. |
| PD | Predicate disambiguation | batch_69d85ddc780481909a3bc63832fe2bd2 |
completed | April 10, 2026, 2:18 a.m. |
| PDg | Predicate description generation | batch_69d87f30642c8190ad94fa061cde186b |
completed | April 10, 2026, 4:40 a.m. |
Created at: April 8, 2026, 9:39 p.m.