Triple
T21594134
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leonard Skinner |
E532853
|
entity |
| Predicate | enforcedRule |
P113152
|
FINISHED |
| Object | strict hair-length rules for male students |
—
|
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: strict hair-length rules for male students | Statement: [Leonard Skinner, enforcedRule, strict hair-length rules for male students]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: enforcedRule Context triple: [Leonard Skinner, enforcedRule, strict hair-length rules for male students]
-
A.
enforcesRule
chosen
Indicates that one entity compels or ensures that another entity follows or complies with a specified rule or set of rules.
-
B.
enforcedOn
Indicates that a rule, policy, or constraint is applied with authority to a particular target or subject.
-
C.
enforcedProvision
Indicates that an authority or agent compelled compliance with a specific rule, term, or provision.
-
D.
alsoEnforcedBy
Indicates that the same rule, policy, or constraint is enforced by an additional authority, mechanism, or entity beyond the primary one.
-
E.
enforcedLaw
Indicates that an authority actively applies or upholds a specific law to regulate behavior or resolve situations.
- 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_69e0c46251648190876f0427cf2d321b |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eefadf6e608190b42b26ea22c76ec6 |
completed | April 27, 2026, 5:57 a.m. |
| PD | Predicate disambiguation | batch_69e632109d048190b4ac3f14fe48d1a0 |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:32 p.m.