Triple
T31176696
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Temple of Hera I |
E794768
|
entity |
| Predicate | numberOfColumnsOnFlank |
P9589
|
FINISHED |
| Object | 18 |
—
|
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: 18 | Statement: [Temple of Hera I, numberOfColumnsOnFlank, 18]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfColumnsOnFlank Context triple: [Temple of Hera I, numberOfColumnsOnFlank, 18]
-
A.
numberOfColumnsOnFlanks
chosen
Indicates the count of columns located on the flanking sides of a structure or object.
-
B.
numberOfColumnsInColonnade
Indicates the count of individual columns that make up a given colonnade.
-
C.
numberOfColumnsOnFacade
Indicates the count of vertical structural or decorative divisions (columns) present on a building’s facade.
-
D.
numberOfColumnsPerShortSide
Indicates the count of columns that appear along each of the shorter sides of a rectangular or similarly shaped structure or layout.
-
E.
numberOfColumns
Indicates the total count of vertical divisions (columns) associated with or contained in a given structure or dataset.
- F. None of above.
Provenance (3 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_69f224d5b9708190b6ca79ad2fd3a28a |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a00c9ddd62881909859a36b114a4132 |
completed | May 10, 2026, 6:09 p.m. |
| PD | Predicate disambiguation | batch_6a00c939b88881909d5353db4265e572 |
completed | May 10, 2026, 6:06 p.m. |
Created at: April 29, 2026, 9:08 p.m.