Triple
T37113245
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Our Lady of Lourdes Church Orlem |
E919049
|
entity |
| Predicate | hasNeighbourhoodName |
P4813
|
FINISHED |
| Object | Orlem Church area |
—
|
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: Orlem Church area | Statement: [Our Lady of Lourdes Church Orlem, hasNeighbourhoodName, Orlem Church area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNeighbourhoodName Context triple: [Our Lady of Lourdes Church Orlem, hasNeighbourhoodName, Orlem Church area]
-
A.
hasNeighbourhood
chosen
Indicates that one entity is located within, or is associated with, a particular neighborhood area of another entity.
-
B.
nicknameOfNeighborhoodItMarks
Indicates that something serves as a marker or reference for a neighborhood by bearing or using that neighborhood’s nickname.
-
C.
hasAreaName
Indicates that an entity is associated with a specific named geographic or administrative area.
-
D.
hasNeighbourhoodStatus
Indicates that an entity has a particular status, classification, or condition specifically in relation to a given neighbourhood.
-
E.
hasNeighbourhoodCount
Indicates the number of neighbourhoods associated with a given entity.
- 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_69f76e9c57148190ba789dd059645bb9 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a0082989e6c819099fea0e706332e37 |
completed | May 10, 2026, 1:05 p.m. |
| PD | Predicate disambiguation | batch_6a0081ed6a1481908baf472876e28db9 |
completed | May 10, 2026, 1:02 p.m. |
Created at: May 3, 2026, 4:15 p.m.