Triple
T9896608
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Torphichen |
E182189
|
entity |
| Predicate | hasPreceptoryFunction |
P91037
|
FINISHED |
| Object | headquarters of Knights Hospitaller in Scotland |
—
|
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: headquarters of Knights Hospitaller in Scotland | Statement: [Torphichen, hasPreceptoryFunction, headquarters of Knights Hospitaller in Scotland]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPreceptoryFunction Context triple: [Torphichen, hasPreceptoryFunction, headquarters of Knights Hospitaller in Scotland]
-
A.
hasClericalFunction
Indicates that an entity performs, is responsible for, or is associated with a clerical or administrative function.
-
B.
hasClericalStructure
Indicates that an entity possesses an organized clerical or administrative hierarchy or framework.
-
C.
containsMonastery
Indicates that one entity includes or encompasses a monastery within its boundaries or composition.
-
D.
hasNearbyMonastery
Indicates that one entity is located close to or in the vicinity of a monastery.
-
E.
hasSacristy
Indicates that a religious building includes or is associated with a sacristy (a room where sacred vessels, vestments, and liturgical items are kept and prepared).
- 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_69ca82876f8081909cf75df0f99bb13f |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cdb4ab15f881909f75a8e01051dbc4 |
completed | April 2, 2026, 12:13 a.m. |
| PD | Predicate disambiguation | batch_69cd1d872d50819096b7ab166a8decf1 |
completed | April 1, 2026, 1:28 p.m. |
| PDg | Predicate description generation | batch_69cd3581a9688190a00cef4c3eebb0ae |
completed | April 1, 2026, 3:10 p.m. |
Created at: March 30, 2026, 8:40 p.m.