Triple
T32993236
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Begijnhof Chapel |
E844147
|
entity |
| Predicate | hasHiddenEntrance |
P197745
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Begijnhof Chapel, hasHiddenEntrance, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHiddenEntrance Context triple: [Begijnhof Chapel, hasHiddenEntrance, true]
-
A.
hasEntrancesIn
Indicates that an entity has one or more entrances located within or opening into another specified entity or area.
-
B.
hasEntranceGimmick
Indicates that an entity features a distinctive or notable gimmick, stunt, or special element associated with its entrance or initial appearance.
-
C.
hasEntrance
Indicates that one entity possesses or provides an entry point or access way to another entity or space.
-
D.
hasEntranceOn
Indicates that one entity’s entrance or access point is located on or faces a specified side, boundary, or feature of another entity.
-
E.
hasNumberOfFalseDoors
Indicates that an entity is associated with a specific count of false doors (door-like architectural features that do not function as actual entrances or exits).
- 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_69f3494d99988190b502c68926af2c4d |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fea5e828cc8190a9b755a645dc56d2 |
completed | May 9, 2026, 3:11 a.m. |
| PD | Predicate disambiguation | batch_69fea36443f08190b2aced9b4a0525fd |
completed | May 9, 2026, 3 a.m. |
| PDg | Predicate description generation | batch_69fea5e75a5481908a04d91ee7255ebb |
completed | May 9, 2026, 3:11 a.m. |
Created at: May 1, 2026, 1:22 a.m.