Triple
T5872946
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CPMP |
E130560
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | EMA |
E23502
|
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: EMA | Statement: [CPMP, partOf, EMA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: EMA Context triple: [CPMP, partOf, EMA]
-
A.
EMA
chosen
EMA is the European Union’s regulatory authority responsible for the scientific evaluation, supervision, and safety monitoring of medicines.
-
B.
EMA
EMA is the three-letter IATA airport code for East Midlands Airport in England, which serves the East Midlands region with domestic and international flights.
-
C.
EMA
EMA is the abbreviated name for the Joint Staff headquarters that oversees and coordinates the operations of the French Armed Forces.
-
D.
Em
Em is a common shortened form of the given name Emma, often used as an informal nickname.
-
E.
EMS
EMS is an emergency medical services organization that provides pre-hospital care and ambulance transport in response to medical emergencies.
- 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_69c0085047dc8190af24e311edad3c07 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c035f99b788190b5b06d83b9499bfa |
completed | March 22, 2026, 6:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0b11b48d88190ba6cd5ade2f47a89 |
completed | March 23, 2026, 3:18 a.m. |
Created at: March 22, 2026, 3:56 p.m.