Triple
T6253952
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Murder of Thomas Becket |
E140113
|
entity |
| Predicate | hasLegalCharacterization |
P13138
|
FINISHED |
| Object | sacrilegious murder |
—
|
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: sacrilegious murder | Statement: [Murder of Thomas Becket, hasLegalCharacterization, sacrilegious murder]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLegalCharacterization Context triple: [Murder of Thomas Becket, hasLegalCharacterization, sacrilegious murder]
-
A.
hasLegalCodeCharacteristic
Indicates that a legal code possesses a specified characteristic, feature, or property.
-
B.
legalCharacterization
chosen
Indicates how an action, event, or situation is classified or characterized under a specific legal framework or set of laws.
-
C.
hasLegalSubject
Indicates that an entity serves as the legal subject (e.g., rights-holder or obligated party) in a legal relationship or context.
-
D.
hasLegalRank
Indicates that an entity holds a specific legal status, classification, or rank within a formal legal or regulatory system.
-
E.
legalCharacter
Indicates that an entity possesses a status, role, or nature that is recognized and defined by law.
- 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_69c008b4858c819095b0199114a9a87b |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c063625608819081f5422112c80ce5 |
completed | March 22, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69c05605566c81908e197f5accd072d2 |
completed | March 22, 2026, 8:50 p.m. |
Created at: March 22, 2026, 4:24 p.m.