Triple
T10709225
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red Line (Chicago "L") |
E252488
|
entity |
| Predicate | lineNumber |
P1864
|
FINISHED |
| Object | Red |
E227272
|
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: Red | Statement: [Red Line (Chicago "L"), lineNumber, Red]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Red Context triple: [Red Line (Chicago "L"), lineNumber, Red]
-
A.
Red
Red is the nickname of William L. "Red" Whittaker, a pioneering American roboticist known for his work in field robotics and autonomous vehicles.
-
B.
Red
Red is the nickname of Red Rolfe, an American Major League Baseball third baseman best known for his years with the New York Yankees in the 1930s and 1940s.
-
C.
Red
chosen
Red is Taylor Swift’s critically acclaimed 2012 studio album that marked her transition from country to mainstream pop with emotionally charged, genre-blending songs.
-
D.
Red
Red is the nickname of Red Cashion, a well-known former American football official in the National Football League.
-
E.
Red
Red is the tough, sharp-tongued Russian matriarch and prison cook from the television series "Orange Is the New Black."
- 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_69d6aa5cbabc8190973e683950d89faf |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fe5063bc8190ba12fd68a59c9a03 |
completed | April 9, 2026, 1:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d9990f220081909dae41bcec8b4768 |
completed | April 11, 2026, 12:42 a.m. |
Created at: April 8, 2026, 9:13 p.m.