Triple
T11482242
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Romero y Galdámez |
E272179
|
entity |
| Predicate | notableBearerReligion |
P25219
|
FINISHED |
| Object | Roman Catholicism |
—
|
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: Roman Catholicism | Statement: [Romero y Galdámez, notableBearerReligion, Roman Catholicism]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableBearerReligion Context triple: [Romero y Galdámez, notableBearerReligion, Roman Catholicism]
-
A.
bearerReligion
chosen
Indicates that a bearer (such as a person or entity) adheres to, practices, or is associated with a particular religion.
-
B.
hasFamousReligiousBearer
Indicates that an entity has at least one well-known religious figure associated with or bearing its name.
-
C.
religiousInstitutionsNotableFor
Indicates that certain religious institutions are notable or distinguished for a particular characteristic, quality, activity, or role.
-
D.
religionNote
Indicates that there is an explanatory note or comment providing additional information or clarification about a subject’s religion.
-
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_69d6aae1b09881909ce2ded3fa0c14fa |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d85a1d86b88190b180f6b0d0a27029 |
completed | April 10, 2026, 2:02 a.m. |
| PD | Predicate disambiguation | batch_69d8086ecd6c81908f424864857762d6 |
completed | April 9, 2026, 8:13 p.m. |
Created at: April 8, 2026, 9:36 p.m.