Triple
T10465634
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Green Man |
E246786
|
entity |
| Predicate | recordLabel |
P1500
|
FINISHED |
| Object | BMG |
E135662
|
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: BMG | Statement: [Green Man, recordLabel, BMG]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: BMG Context triple: [Green Man, recordLabel, BMG]
-
A.
BMG
BMG is the commonly used abbreviation for Germany’s Federal Ministry of Health.
-
B.
BMG
chosen
BMG is a major global music company known for its music publishing and recorded music services, representing a wide range of international artists and catalogs.
-
C.
BMG
BMG is the IATA airport code for Monroe County Airport serving Bloomington, Indiana.
-
D.
BMG Classics
BMG Classics was the classical music division and record label of Bertelsmann Music Group, known for releasing and distributing classical recordings by major orchestras, conductors, and soloists.
-
E.
BMG India
BMG India was the Indian division of the global music company Bertelsmann Music Group, responsible for producing and distributing music in the Indian market.
- 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_69d381c16c248190a2fe5b471e584e9c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5092d6d408190b6bda4d7ced4601e |
completed | April 7, 2026, 1:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d89fe6129881908c658ff977e68135 |
completed | April 10, 2026, 6:59 a.m. |
Created at: April 6, 2026, 12:19 p.m.