Triple
T9054947
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Murder of Geta |
E216973
|
entity |
| Predicate | hasManner |
P86122
|
FINISHED |
| Object | killing in a supposed reconciliation meeting |
—
|
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: killing in a supposed reconciliation meeting | Statement: [Murder of Geta, hasManner, killing in a supposed reconciliation meeting]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasManner Context triple: [Murder of Geta, hasManner, killing in a supposed reconciliation meeting]
-
A.
hasPolitePronoun
Indicates that one entity refers to another using a polite or honorific form of address in language.
-
B.
hasPolitenessSystem
Indicates that a language or communication system includes formalized ways of expressing different levels of politeness or social hierarchy.
-
C.
hasMoustache
Indicates that an entity possesses a moustache.
-
D.
politenessLevel
Indicates the degree of courteousness or respectfulness expressed by one entity toward another in an interaction.
-
E.
hasPar
Indicates a relationship where one entity has another entity as its parent.
- 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_69ca83d4425481909a319dab847724ec |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc7a7488188190b3dd6bc2f2377503 |
completed | April 1, 2026, 1:52 a.m. |
| PD | Predicate disambiguation | batch_69cc5ee6d83c819095d8ed0779aa8511 |
completed | March 31, 2026, 11:55 p.m. |
| PDg | Predicate description generation | batch_69cc5f4f1cb48190a025d1b3d8d7a790 |
completed | March 31, 2026, 11:57 p.m. |
Created at: March 30, 2026, 7:10 p.m.