Triple
T27031185
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meathead |
E680921
|
entity |
| Predicate | conflictTopic |
P163904
|
FINISHED |
| Object | social issues |
—
|
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: social issues | Statement: [Meathead, conflictTopic, social issues]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: conflictTopic Context triple: [Meathead, conflictTopic, social issues]
-
A.
conflictIn
Indicates that one entity is involved in, associated with, or occurs within a particular conflict or dispute.
-
B.
conflictSpecific
Indicates a specific, concrete instance or type of conflict that exists between the related entities.
-
C.
mainConflict
Indicates the primary opposing force, problem, or struggle that drives tension and narrative progression between entities or sides.
-
D.
conflictType
Indicates the specific kind or category of conflict that characterizes the relationship or interaction between entities.
-
E.
conflictIntroduced
Indicates that a conflict has been newly created or brought into existence between the related entities.
- 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_69eeeb5566f08190813daf896fa3da04 |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f6416fbf4081909b0913c337927fc4 |
completed | May 2, 2026, 6:24 p.m. |
| PD | Predicate disambiguation | batch_69f63c663be481908f233d25d28713a4 |
completed | May 2, 2026, 6:03 p.m. |
| PDg | Predicate description generation | batch_69f63fd4f7448190930c723ba7cfce62 |
completed | May 2, 2026, 6:17 p.m. |
Created at: April 27, 2026, 7:14 a.m.