Triple
T27427431
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Xcalakoop San Juan Bautista |
E690527
|
entity |
| Predicate | hasReligiousReferenceInName |
P139594
|
FINISHED |
| Object | Saint John the Baptist |
—
|
NE NERFINISHED |
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: Saint John the Baptist | Statement: [Xcalakoop San Juan Bautista, hasReligiousReferenceInName, Saint John the Baptist]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasReligiousReferenceInName Context triple: [Xcalakoop San Juan Bautista, hasReligiousReferenceInName, Saint John the Baptist]
-
A.
hasReligiousAffiliationInName
Indicates that an entity’s name explicitly includes or reflects a religious affiliation or association.
-
B.
hasReligiousName
Indicates that an entity possesses a name specifically associated with a religious context, role, or tradition.
-
C.
hasReligiousToponym
chosen
Indicates that a place name is derived from or explicitly references a religious figure, concept, institution, or tradition.
-
D.
religiousName
Indicates that an entity has or is known by a name specifically associated with a religious role, identity, or context.
-
E.
hasEponymReligion
Indicates that a religion is named after or derived from the name of a particular person.
- 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_69ef52003fb48190b0f1295246182a86 |
completed | April 27, 2026, 12:09 p.m. |
| NER | Named-entity recognition | batch_69fec25f0fc48190b87ab1f9cd1eb0de |
completed | May 9, 2026, 5:13 a.m. |
| PD | Predicate disambiguation | batch_69fec079a770819098df7cc3049df954 |
completed | May 9, 2026, 5:04 a.m. |
Created at: April 27, 2026, 12:41 p.m.