Triple
T21446723
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shermer, Illinois |
E529095
|
entity |
| Predicate | hasFictionalPostalArea |
P21117
|
FINISHED |
| Object | Chicago suburbs |
—
|
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: Chicago suburbs | Statement: [Shermer, Illinois, hasFictionalPostalArea, Chicago suburbs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalPostalArea Context triple: [Shermer, Illinois, hasFictionalPostalArea, Chicago suburbs]
-
A.
hasFictionalPostcode
Indicates that an entity is associated with a postcode that is invented or not used in the real-world postal system.
-
B.
hasFictionalAddressTown
Indicates that an entity is associated with a town that serves as its fictional address location.
-
C.
hasFictionalLocation
chosen
Indicates that an entity is associated with, set in, or takes place within a location that exists only in fiction rather than in the real world.
-
D.
hasFictionalCounty
Indicates that one entity includes, is set in, or is associated with a county that is fictional rather than real.
-
E.
hasFictionalAddressStatus
Indicates that an entity’s address is designated as fictional rather than a real-world, verifiable location.
- 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_69e0c457579481909db68053ed99750c |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9e9cea7bc81909ee3e1cdeda1fe7e |
completed | April 23, 2026, 9:43 a.m. |
| PD | Predicate disambiguation | batch_69e631df1b38819088d3604854e697b4 |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:06 p.m.