Triple
T26395235
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Touhy Avenue, Niles, Illinois |
E663528
|
entity |
| Predicate | hasNotableLandmarkAlong |
P153329
|
FINISHED |
| Object | Leaning Tower of Niles |
—
|
NE NERFINISHED |
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: Leaning Tower of Niles | Statement: [Touhy Avenue, Niles, Illinois, hasNotableLandmarkAlong, Leaning Tower of Niles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableLandmarkAlong Context triple: [Touhy Avenue, Niles, Illinois, hasNotableLandmarkAlong, Leaning Tower of Niles]
-
A.
hasNotableScenicSpot
Indicates that an entity possesses or is associated with a particularly remarkable or well-known scenic location.
-
B.
containsNotablePlace
chosen
Indicates that one location includes within its boundaries a place that is considered notable or significant.
-
C.
hasNotableToponym
Indicates that an entity is associated with a place name that is particularly notable, distinctive, or significant.
-
D.
hasLandmarkCity
Indicates that a particular landmark is located within or associated with a specific city.
-
E.
hasNotableGovernmentBuilding
Indicates that a location possesses a government building that is significant or prominent in some recognized way.
- 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_69ee883823988190b418b111be28a44a |
completed | April 26, 2026, 9:48 p.m. |
| NER | Named-entity recognition | batch_69fd64bc86848190a49f451a8fc5cf1e |
completed | May 8, 2026, 4:21 a.m. |
| PD | Predicate disambiguation | batch_69fd5ff4a648819090756d90fd195d9a |
completed | May 8, 2026, 4 a.m. |
Created at: April 26, 2026, 11:28 p.m.