Triple
T765049
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Temple of Athena Nike |
E16156
|
entity |
| Predicate | hasColumnCountBack |
P18958
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Temple of Athena Nike, hasColumnCountBack, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasColumnCountBack Context triple: [Temple of Athena Nike, hasColumnCountBack, 4]
-
A.
numberOfColumns
Indicates the total count of vertical divisions (columns) associated with or contained in a given structure or dataset.
-
B.
hasColumns
Indicates that one entity possesses or is characterized by a set of columns associated with it.
-
C.
numberOfColumnsOnFacade
Indicates the count of vertical structural or decorative divisions (columns) present on a building’s facade.
-
D.
hasSectionCount
Indicates that an entity is associated with a specific number of sections it contains or comprises.
-
E.
numberOfColumnsOnFlanks
Indicates the count of columns located on the flanking sides of a structure or object.
- 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_69a493684ee48190bd43b7c78da4aec8 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a69dfeb08190b54a476cfa66e6d6 |
completed | March 1, 2026, 8:50 p.m. |
| PD | Predicate disambiguation | batch_69a4a506106081909ef97a679ff00a5a |
completed | March 1, 2026, 8:43 p.m. |
| PDg | Predicate description generation | batch_69a4a5a35c68819082429755c046e9a7 |
completed | March 1, 2026, 8:46 p.m. |
Created at: March 1, 2026, 7:37 p.m.