Triple
T17886672
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Al Pratt |
E447216
|
entity |
| Predicate | relationshipToDamage |
P78831
|
FINISHED |
| Object | godfather |
—
|
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: godfather | Statement: [Al Pratt, relationshipToDamage, godfather]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToDamage Context triple: [Al Pratt, relationshipToDamage, godfather]
-
A.
damageTo
Indicates a relationship where one entity causes harm, loss, or deterioration to another entity.
-
B.
victimRelation
Indicates that one entity is the victim or target of harm, wrongdoing, or an adverse action caused by another entity.
-
C.
damageAssociatedWith
chosen
Indicates a relationship where one entity is linked to causing, contributing to, or being responsible for damage affecting another entity.
-
D.
victimRelationship
Indicates that one entity is the victim in relation to another entity involved in a harmful, criminal, or adverse act.
-
E.
relationshipImpact
Indicates how one entity’s relationship with another affects or changes those entities or their interaction.
- 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_69d8b9f59bd48190a6fc925a855b8bac |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49c13bd288190a0302010ba4d0532 |
completed | April 19, 2026, 9:10 a.m. |
| PD | Predicate disambiguation | batch_69e3d8e9b77c8190bbfb508f28dfacfa |
completed | April 18, 2026, 7:18 p.m. |
Created at: April 10, 2026, 10:18 a.m.