Triple
T21449423
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carole Bayer Sager |
E529168
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Bayer Sager |
—
|
NE NERFINISHED |
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: Bayer Sager | Statement: [Carole Bayer Sager, familyName, Bayer Sager]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bayer Sager Context triple: [Carole Bayer Sager, familyName, Bayer Sager]
-
A.
Bayer Sager
chosen
Bayer Sager is the surname of Carole Bayer Sager, an American songwriter known for numerous pop hits and award-winning collaborations.
-
B.
Schenker AG
Schenker AG is a global logistics and freight forwarding company headquartered in Germany and operating under the DB Schenker brand.
-
C.
Buna-Werke
Buna-Werke was a synthetic rubber and fuel plant operated by IG Farben near Auschwitz, notorious for its use of forced labor from the adjacent Monowitz concentration camp during World War II.
-
D.
Bachem-Werke
Bachem-Werke was a German aerospace manufacturer best known for producing experimental rocket-powered aircraft during World War II.
-
E.
Bayer
Bayer is a major German multinational pharmaceutical and life sciences company known for products such as aspirin and its work in healthcare and agriculture.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e9e9d11ca48190aafe25c97dfa5578 |
completed | April 23, 2026, 9:43 a.m. |
Created at: April 16, 2026, 6:06 p.m.