Triple
T34801586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Murderers' Row |
E1003230
|
entity |
| Predicate | offensiveCategory |
P181492
|
FINISHED |
| Object | run scoring |
—
|
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: run scoring | Statement: [Murderers' Row, offensiveCategory, run scoring]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: offensiveCategory Context triple: [Murderers' Row, offensiveCategory, run scoring]
-
A.
offensiveCharacteristic
Indicates that one entity possesses a trait, behavior, or quality that is considered insulting, disrespectful, or likely to cause offense to another entity or group.
-
B.
offense
Indicates that one entity commits, causes, or is responsible for a violation, wrongdoing, or rule-breaking act against another entity or governing norms.
-
C.
offenseAgainst
Indicates that one party has committed a harmful, illegal, or rule-violating act directed against another party or entity.
-
D.
offensiveFollowed
Indicates that one entity follows another in a manner perceived as offensive, hostile, or harassing.
-
E.
offensiveStrategy
Indicates a strategic approach focused on attacking or aggressively advancing against an opponent.
- 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_69f76db543808190b188c6c86a91491b |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69f77ab085088190ace5734dcc9f1167 |
completed | May 3, 2026, 4:41 p.m. |
| PD | Predicate disambiguation | batch_69f7795b1abc8190823664d1caa94649 |
completed | May 3, 2026, 4:35 p.m. |
| PDg | Predicate description generation | batch_69f77a39135081908ae22d2a23b44e74 |
completed | May 3, 2026, 4:39 p.m. |
Created at: May 3, 2026, 3:59 p.m.