Triple
T9753179
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roz |
E236489
|
entity |
| Predicate | antagonisticToward |
P21197
|
FINISHED |
| Object | Mike Wazowski's lax paperwork habits |
—
|
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: Mike Wazowski's lax paperwork habits | Statement: [Roz, antagonisticToward, Mike Wazowski's lax paperwork habits]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: antagonisticToward Context triple: [Roz, antagonisticToward, Mike Wazowski's lax paperwork habits]
-
A.
antagonistOf
Indicates a relationship where one entity actively opposes, conflicts with, or serves as an adversary to another.
-
B.
belligerentAgainst
chosen
Indicates a hostile or aggressive stance, conflict, or antagonistic behavior directed by one entity against another.
-
C.
opposite
Indicates that one entity is positioned or oriented directly across from, or in a contrary or reverse relation to, another entity.
-
D.
opposedQualityTo
Indicates that one quality stands in direct opposition or contrast to another quality.
-
E.
antagonistInvolved
Indicates that an antagonist participates in, influences, or is otherwise actively involved in the referenced event or situation.
- 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_69ca84d4eddc8190996fec1417d2bae8 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9fae332c8190b11f0258b5a5ae2b |
completed | April 1, 2026, 10:43 p.m. |
| PD | Predicate disambiguation | batch_69cd03cc128c81908b84ef224f858b4e |
completed | April 1, 2026, 11:38 a.m. |
Created at: March 30, 2026, 8:24 p.m.