Triple
T8371600
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Case Red |
E197472
|
entity |
| Predicate | follows |
P134
|
FINISHED |
| Object | Case Yellow |
E38765
|
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: Case Yellow | Statement: [Case Red, follows, Case Yellow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Case Yellow Context triple: [Case Red, follows, Case Yellow]
-
A.
Case Yellow
chosen
Case Yellow was the codename for Nazi Germany’s 1940 military campaign that rapidly conquered France and the Low Countries during World War II.
-
B.
Case Blue
Case Blue was the German Wehrmacht’s 1942 summer offensive on the Eastern Front aimed at seizing the oil-rich Caucasus and advancing toward Stalingrad during World War II.
-
C.
Yallow
Yallow is a surname associated with the individual known by the name or handle "w1n5t0n."
-
D.
Žut
Žut is a largely uninhabited, rugged Adriatic island in Croatia known for its coves, clear waters, and popularity among sailors and boaters.
-
E.
Gelb
Gelb is a surname most prominently associated with Peter Gelb, the influential general manager of the Metropolitan Opera in New York City.
- 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_69ca82f56730819080cec5d991c76f4c |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb80a509dc81909e0ea4c66b21d84f |
completed | March 31, 2026, 8:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cde7d0a6d081909ffe138f80605992 |
completed | April 2, 2026, 3:51 a.m. |
Created at: March 30, 2026, 6:01 p.m.