Triple
T1051920
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Holy Trinity Monastery |
E22716
|
entity |
| Predicate | hasCells |
P23615
|
FINISHED |
| Object | monks' cells |
—
|
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: monks' cells | Statement: [Holy Trinity Monastery, hasCells, monks' cells]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCells Context triple: [Holy Trinity Monastery, hasCells, monks' cells]
-
A.
numberOfCells
Indicates the total count of individual cells associated with or contained in a given entity.
-
B.
hasColumns
Indicates that one entity possesses or is characterized by a set of columns associated with it.
-
C.
hasCollection
Indicates that an entity possesses, maintains, or is associated with a set or group of related items treated as a collection.
-
D.
hasColumnCountBack
Indicates that an entity (such as a table or layout) has a specified number of columns on its back side or rear-facing section.
-
E.
hasComponentCount
Indicates that an entity is associated with a specific number of components it contains or comprises.
- 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_69a493da02e081908c13ff5e02a0fe7a |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b8b5312081909796df58fa7c1e9d |
completed | March 1, 2026, 10:07 p.m. |
| PD | Predicate disambiguation | batch_69a4b7309cc481908ed839b0b8d75dbf |
completed | March 1, 2026, 10:01 p.m. |
| PDg | Predicate description generation | batch_69a4b7f2d1b081908eb2df54e91c8c1d |
completed | March 1, 2026, 10:04 p.m. |
Created at: March 1, 2026, 7:42 p.m.