Triple
T1309113
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Church of Saint Anne in Jerusalem |
E27947
|
entity |
| Predicate | hasAisles |
P27817
|
FINISHED |
| Object | two side aisles |
—
|
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: two side aisles | Statement: [Church of Saint Anne in Jerusalem, hasAisles, two side aisles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAisles Context triple: [Church of Saint Anne in Jerusalem, hasAisles, two side aisles]
-
A.
hasShoppingMall
Indicates that one entity possesses, contains, or includes a shopping mall within its area or domain.
-
B.
hasRetailArea
Indicates that an entity possesses or includes a designated space used for retail or commercial sales activities.
-
C.
hasNumberOfEntrances
Indicates the relationship that specifies how many entrances an entity possesses.
-
D.
hasShop
Indicates that one entity owns, operates, or is associated with a shop or retail establishment.
-
E.
hasUpperFloor
Indicates that one entity possesses or includes an upper floor relative to another level or reference point.
- F. None of above. chosen
Provenance (4 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_69a496d7d83481908f83085854e51328 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c15490a88190872c3d2698a8f9c9 |
completed | March 1, 2026, 10:44 p.m. |
| PD | Predicate disambiguation | batch_69a4bee9e4a88190b22ab2ee831a23c9 |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4c15361c8819094b8171e780b5560 |
completed | March 1, 2026, 10:44 p.m. |
Created at: March 1, 2026, 7:51 p.m.