Triple
T38324632
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martyrdom of the Holy Queen Shushanik |
E1036749
|
entity |
| Predicate | characterRoleOfShushanik |
P23263
|
FINISHED |
| Object | noblewoman |
—
|
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: noblewoman | Statement: [Martyrdom of the Holy Queen Shushanik, characterRoleOfShushanik, noblewoman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterRoleOfShushanik Context triple: [Martyrdom of the Holy Queen Shushanik, characterRoleOfShushanik, noblewoman]
-
A.
describesCharacterRole
Indicates that one entity specifies or defines the narrative or functional role played by another entity.
-
B.
characterInWorkDescribedAs
Indicates that a character is portrayed or described in a particular way within a specific work.
-
C.
roleInShu
Indicates that an entity holds or plays a specific role within the context of Shu (e.g., the state, domain, or system referred to as Shu).
-
D.
featuresCharacterRole
chosen
Indicates that a work includes a character appearing in a specific narrative or functional role.
-
E.
roleInSyriana
Indicates that one entity has a specific role or involvement in the context of "Syriana," such as participation, function, or contribution related to it.
- 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_69f76e1c16fc8190bde982289dd5106b |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a0018cf6ebc8190aee6288788d0067e |
completed | May 10, 2026, 5:34 a.m. |
| PD | Predicate disambiguation | batch_6a001855e8588190a65840485473cf8b |
completed | May 10, 2026, 5:32 a.m. |
Created at: May 3, 2026, 4:30 p.m.