Triple
T32208738
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Concepción |
E822741
|
entity |
| Predicate | religiousNameUsage |
P128667
|
FINISHED |
| Object | sometimes given in honor of the Virgin Mary |
—
|
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: sometimes given in honor of the Virgin Mary | Statement: [Concepción, religiousNameUsage, sometimes given in honor of the Virgin Mary]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: religiousNameUsage Context triple: [Concepción, religiousNameUsage, sometimes given in honor of the Virgin Mary]
-
A.
religiousName
Indicates that an entity has or is known by a name specifically associated with a religious role, identity, or context.
-
B.
isUsedByReligion
Indicates that something (such as an object, practice, text, or symbol) is employed or utilized within the context of a particular religion.
-
C.
traditionalReligionName
Indicates that an entity has a name specifically associated with a traditional or indigenous religion.
-
D.
religionName
Indicates the religious affiliation or belief system associated with an entity.
-
E.
hasReligiousAffiliationInName
chosen
Indicates that an entity’s name explicitly includes or reflects a religious affiliation or association.
- 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_69f3490a3bec819097bc58d4731b9d08 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fed83b1d188190a318b0ad3003200a |
completed | May 9, 2026, 6:46 a.m. |
| PD | Predicate disambiguation | batch_69fed78e03548190b6e6ad93ae8d131d |
completed | May 9, 2026, 6:43 a.m. |
Created at: May 1, 2026, 12:37 a.m.