Triple
T2490400
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old City Hall (Toronto) |
E52025
|
entity |
| Predicate | clockTowerHeight |
P18664
|
FINISHED |
| Object | approximately 103.6 metres |
—
|
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: approximately 103.6 metres | Statement: [Old City Hall (Toronto), clockTowerHeight, approximately 103.6 metres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: clockTowerHeight Context triple: [Old City Hall (Toronto), clockTowerHeight, approximately 103.6 metres]
-
A.
hasClockTower
Indicates that one entity possesses or features a clock tower as part of its structure or property.
-
B.
towerLocation
Indicates that a tower is located at, or associated with, a specific place or geographic location.
-
C.
towerName
Indicates the specific name assigned to a tower in the relationship.
-
D.
hasTowerHeight
chosen
Indicates that an entity (such as a tower or structure) has a specific height value associated with it.
-
E.
towerType
Indicates the specific kind or classification of a tower that an entity is associated with or represents.
- 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_69ab4955111c8190835bf619adec21ff |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd18fe32081909580c6272a6013c5 |
completed | March 7, 2026, 7:19 a.m. |
| PD | Predicate disambiguation | batch_69abd0b980b481908d4932bcea4a6167 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:45 p.m.