Triple
T1826869
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Case Blue |
E40672
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | Fall Blau |
E20942
|
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: Fall Blau | Statement: [Case Blue, alsoKnownAs, Fall Blau]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fall Blau Context triple: [Case Blue, alsoKnownAs, Fall Blau]
-
A.
Fall Blau
chosen
Fall Blau was the German Wehrmacht’s 1942 summer offensive on the Eastern Front aimed at capturing the oil fields of the Caucasus and advancing toward Stalingrad during World War II.
-
B.
Fall Weiss
Fall Weiss was the codename for Nazi Germany’s military plan to invade Poland in September 1939, marking the beginning of World War II in Europe.
-
C.
“Fall”
“Fall” is a globally popular Afrobeats hit by Nigerian singer Davido, known for its catchy melody and massive international streaming success.
-
D.
Blau
The Blau is a small river in the German state of Baden-Württemberg that flows through the city of Blaustein before joining the Danube.
-
E.
Bluets
Bluets is a lyrical, genre-defying book by Maggie Nelson that blends memoir, philosophy, and poetic meditation through an obsessive focus on the color blue.
- 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_69a8864644bc8190b2358ab897194ac1 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb01022108190b1da05a31454ab8d |
completed | March 7, 2026, 4:56 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adbf6b4bdc8190b53dbdc9c31e3685 |
completed | March 8, 2026, 6:26 p.m. |
Created at: March 4, 2026, 7:32 p.m.