Triple
T23038295
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ntem |
E573667
|
entity |
| Predicate | countryCode |
P208
|
FINISHED |
| Object | CMR |
—
|
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: CMR | Statement: [Ntem, countryCode, CMR]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CMR Context triple: [Ntem, countryCode, CMR]
-
A.
CMR
CMR is the station code assigned to Colmar railway station in France.
-
B.
CMR
chosen
CMR is the three-letter FIFA country code used to represent the Cameroon national football team in international competitions and rankings.
-
C.
CMR
CMR is the commonly used abbreviation for the Code of Massachusetts Regulations, the official compilation of administrative rules and regulations issued by Massachusetts state agencies.
-
D.
CMR
CMR is an international convention that standardizes conditions and documentation for the cross-border transport of goods by road among its signatory countries.
-
E.
SCMR
SCMR is a professional society dedicated to advancing the field of cardiovascular magnetic resonance imaging through research, education, and clinical practice guidelines.
- 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_69e245b911188190bc3d96326c847969 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f185111f0881908991fcd6cdc7db9f |
completed | April 29, 2026, 4:12 a.m. |
Created at: April 17, 2026, 3:53 p.m.