Triple
T8170771
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | András Schiff |
E190811
|
entity |
| Predicate | recordLabel |
P1500
|
FINISHED |
| Object | Teldec |
E364169
|
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: Teldec | Statement: [András Schiff, recordLabel, Teldec]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Teldec Context triple: [András Schiff, recordLabel, Teldec]
-
A.
Teldec
chosen
Teldec was a prominent German classical music record label known for its high-quality recordings and influential catalog of orchestral and early music.
-
B.
Teklicon
Teklicon is a technology consulting and expert witness firm known for employing semiconductor pioneer Marcian "Ted" Hoff.
-
C.
Nicado
Nicado is a Spanish-language surname most notably borne by Cuban mathematician and academic leader Miriam Nicado García.
-
D.
Zenta
Zenta is a historic town in northern Serbia, best known as the site of a decisive 1697 battle between the Habsburg Monarchy and the Ottoman Empire.
-
E.
Doro
Doro is a diminutive form of the given name Dorothy, often used as a short or affectionate nickname.
- 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_69ca82c1c0a08190bf8692b4d91a03ca |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb48056d0c819094575090a41e0083 |
completed | March 31, 2026, 4:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ccbf5cfd588190b12ef9b5799ffd88 |
completed | April 1, 2026, 6:46 a.m. |
Created at: March 30, 2026, 5:39 p.m.