Triple
T1497086
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Niles, Illinois |
E29710
|
entity |
| Predicate | hasLeaningTowerReplica |
P8955
|
FINISHED |
| Object | replica of the Leaning Tower of Pisa |
—
|
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: replica of the Leaning Tower of Pisa | Statement: [Niles, Illinois, hasLeaningTowerReplica, replica of the Leaning Tower of Pisa]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLeaningTowerReplica Context triple: [Niles, Illinois, hasLeaningTowerReplica, replica of the Leaning Tower of Pisa]
-
A.
hasBellTower
Indicates that one entity (typically a building or structure) possesses or includes a bell tower as part of it.
-
B.
hasClockTower
Indicates that one entity possesses or features a clock tower as part of its structure or property.
-
C.
hasTower
chosen
Indicates that one entity possesses, contains, or is characterized by the presence of a tower.
-
D.
LeonardoDaVinciResidence
Indicates that a specified location served as a place where Leonardo da Vinci lived or resided.
-
E.
hasStatue
Indicates that one entity possesses, contains, or is associated with a statue representing or located within it.
- 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_69a498dba1d8819093b46a3a8d2485f1 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c6edca248190a90205799b270058 |
completed | March 1, 2026, 11:08 p.m. |
| PD | Predicate disambiguation | batch_69a4c48a8cf48190a6ebf8d44a608a06 |
completed | March 1, 2026, 10:58 p.m. |
Created at: March 1, 2026, 8:12 p.m.