Triple
T19928369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Passion of Perpetua and Felicity |
E478986
|
entity |
| Predicate | containsSectionInLanguage |
P137865
|
FINISHED |
| Object | Greek |
—
|
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: Greek | Statement: [Passion of Perpetua and Felicity, containsSectionInLanguage, Greek]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsSectionInLanguage Context triple: [Passion of Perpetua and Felicity, containsSectionInLanguage, Greek]
-
A.
hasSectionIn
Indicates that one entity contains or includes another entity as a section or subdivision within it.
-
B.
hasSectionWith
Indicates that an entity contains or includes a specific section that satisfies certain conditions or characteristics.
-
C.
hasSectionOn
Indicates that one entity (typically a document or resource) contains a dedicated section or part that specifically addresses or discusses another entity or topic.
-
D.
hasSect
Indicates that an entity includes, contains, or is associated with a particular sect or subgroup within a larger religious, ideological, or organizational context.
-
E.
hasSectionInKey
Indicates that a key (such as a document, configuration, or data structure key) contains or is associated with a specific section within it.
- 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_69d8e521855c8190b41871700afc8d6a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e659cc1a448190aa98d4a66022457e |
completed | April 20, 2026, 4:52 p.m. |
| PD | Predicate disambiguation | batch_69e537f070b481908958e0e5911dcdc1 |
completed | April 19, 2026, 8:15 p.m. |
| PDg | Predicate description generation | batch_69e543c136b081909cab9394b958390a |
completed | April 19, 2026, 9:06 p.m. |
Created at: April 10, 2026, 1:53 p.m.