Triple
T25818047
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Velankanni |
E650318
|
entity |
| Predicate | devotionCenteredOn |
P21326
|
FINISHED |
| Object |
Our Lady of Good Health
Our Lady of Good Health is a revered Marian title associated with miraculous healings and a major Catholic pilgrimage shrine in Velankanni, India.
|
E1701926
|
NE FINISHED |
How this triple was built (3 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: Our Lady of Good Health | Statement: [Velankanni, devotionCenteredOn, Our Lady of Good Health]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Our Lady of Good Health Triple: [Velankanni, devotionCenteredOn, Our Lady of Good Health]
Generated description
Our Lady of Good Health is a revered Marian title associated with miraculous healings and a major Catholic pilgrimage shrine in Velankanni, India.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: devotionCenteredOn Context triple: [Velankanni, devotionCenteredOn, Our Lady of Good Health]
-
A.
devotionPracticedBy
Indicates that a particular form of devotion, worship, or religious practice is performed or carried out by a specified agent or practitioner.
-
B.
isDevotedTo
Indicates a strong, enduring commitment or dedication that one entity directs toward another.
-
C.
associatedDevotion
chosen
Indicates a relationship where one entity is linked to or characterized by a particular devotion, dedication, or religious/spiritual practice connected to another entity.
-
D.
evidenceOfDevotion
Indicates that one entity serves as proof or demonstration of another entity’s devotion or commitment.
-
E.
centralActOfWorshipOf
Indicates that one entity serves as the primary or most important act of worship directed toward another entity.
- F. None of above.
Provenance (6 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_69e7ab367fcc8190a5ff1e7f3da046a4 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f600cb37f081908ad2ea805c555876 |
completed | May 2, 2026, 1:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a10eca826ec819087e5031feb0d7960 |
completed | May 22, 2026, 11:54 p.m. |
| NEDg | Description generation | batch_6a10ef87acc4819090ba7e1f69b4c36d |
completed | May 23, 2026, 12:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a10f02156908190aa1fb061ae251f82 |
completed | May 23, 2026, 12:09 a.m. |
| PD | Predicate disambiguation | batch_69f4938b960081909b53c074a3e0c7c2 |
completed | May 1, 2026, 11:50 a.m. |
Created at: April 22, 2026, 7:27 a.m.