Triple
T15241707
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Far South Side |
E364270
|
entity |
| Predicate | hasNeighborhood |
P40
|
FINISHED |
| Object | Hegewisch |
E885901
|
NE 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: Hegewisch | Statement: [Far South Side, hasNeighborhood, Hegewisch]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hegewisch Context triple: [Far South Side, hasNeighborhood, Hegewisch]
-
A.
Hegewisch
chosen
Hegewisch is a far Southeast Side neighborhood of Chicago known for its industrial roots, rail yards, and proximity to the Calumet River and Indiana border.
-
B.
Hollstadt
Hollstadt is a small municipality in the Bavarian district of Rhön-Grabfeld in northern Germany.
-
C.
Waldstadt
Waldstadt is a district of Karlsruhe in the German state of Baden-Württemberg, characterized by its forested setting and primarily residential layout.
-
D.
Weisendorf
Weisendorf is a small municipality in the Erlangen-Höchstadt district of Bavaria, Germany, known for its rural character and proximity to the city of Erlangen.
-
E.
Hofstadt
Hofstadt is the maiden surname of Betty Draper, a central character on the television series "Mad Men."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d85a0dde7481908fc64d1e82d5d20d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e007dcc33081908545ea1a1d2c19fe |
completed | April 15, 2026, 9:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fedd41b7c48190917385c6c61370b2 |
completed | May 9, 2026, 7:07 a.m. |
Created at: April 10, 2026, 3:13 a.m.