Triple
T11456934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gary metropolitan area |
E271551
|
entity |
| Predicate | proximityToMajorCity |
P44123
|
FINISHED |
| Object | near Chicago central business district |
—
|
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: near Chicago central business district | Statement: [Gary metropolitan area, proximityToMajorCity, near Chicago central business district]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: proximityToMajorCity Context triple: [Gary metropolitan area, proximityToMajorCity, near Chicago central business district]
-
A.
distanceFromMajorCity
Indicates the measured distance between a given location and a specified major city.
-
B.
hasNearbyMajorCityCountry
Indicates that an entity has a nearby major city located in the specified country.
-
C.
hasUrbanProximity
chosen
Indicates that one entity is located near or within easy access to an urban area associated with another entity.
-
D.
nearestMajorCity
Indicates that one city is the closest significant urban center to another location or city compared to all other major cities.
-
E.
passesNearCity
Indicates that the path, route, or trajectory of one entity goes close to, but not necessarily through, a specified city.
- 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_69d6aadff8888190a13f253f0d460874 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d822f2138081909408c7916cef99c9 |
completed | April 9, 2026, 10:06 p.m. |
| PD | Predicate disambiguation | batch_69d80867ff248190bb157fa9e355353b |
completed | April 9, 2026, 8:13 p.m. |
Created at: April 8, 2026, 9:35 p.m.