Triple
T30244949
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abronhill |
E769030
|
entity |
| Predicate | hasRomanCatholicPrimarySchool |
P28677
|
FINISHED |
| Object |
St Lucy’s Primary School
St Lucy’s Primary School is a Roman Catholic primary school serving the local community of Abronhill.
|
E1905109
|
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: St Lucy’s Primary School | Statement: [Abronhill, hasRomanCatholicPrimarySchool, St Lucy’s Primary School]
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: St Lucy’s Primary School Triple: [Abronhill, hasRomanCatholicPrimarySchool, St Lucy’s Primary School]
Generated description
St Lucy’s Primary School is a Roman Catholic primary school serving the local community of Abronhill.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRomanCatholicPrimarySchool Context triple: [Abronhill, hasRomanCatholicPrimarySchool, St Lucy’s Primary School]
-
A.
hasCatholicSchoolBoard
Indicates that a given region or jurisdiction is served by, or falls under the authority of, a Catholic school board.
-
B.
hasRomanCatholicChurch
Indicates that one entity possesses, contains, or is the location of a Roman Catholic church.
-
C.
hasParochialSchool
chosen
Indicates that an entity operates, is associated with, or includes a parochial (religious-affiliated) school.
-
D.
hasPrimaryAndSecondarySchool
Indicates that an entity provides or includes both primary-level and secondary-level schooling within its educational offerings.
-
E.
hasPrimaryEducationFacilityType
Indicates the type or category of primary education facility associated with an 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_69f224831dc08190b2e569b987264057 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fce28d6c3081908bf76f5db63ecf68 |
completed | May 7, 2026, 7:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a276451c2988190871b4d5d0bd4dc3f |
completed | June 9, 2026, 12:54 a.m. |
| NEDg | Description generation | batch_6a2764dcc7148190b7ba48ce073f845f |
completed | June 9, 2026, 12:57 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a2765db45d88190817f04133b5efd75 |
completed | June 9, 2026, 1:01 a.m. |
| PD | Predicate disambiguation | batch_69fce12d2f08819082134b5eb3db6a24 |
completed | May 7, 2026, 6:59 p.m. |
Created at: April 29, 2026, 7:39 p.m.