Triple
T301879
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Royal Observer Corps |
E6214
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object | ROC |
E6214
|
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: ROC | Statement: [Royal Observer Corps, abbreviation, ROC]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ROC Context triple: [Royal Observer Corps, abbreviation, ROC]
-
A.
ROC
chosen
ROC is the commonly used abbreviation for the Royal Observer Corps, a former British civil defense organization that monitored aircraft and nuclear explosions during the 20th century.
-
B.
RBM
RBM is a global partnership initiative dedicated to coordinating and scaling up efforts to prevent, control, and ultimately eliminate malaria worldwide.
-
C.
CDF
CDF is the National Rail station code for Cardiff Central railway station, a major transport hub in Cardiff, Wales.
-
D.
OC
OC is the post-nominal designation for Officer of the Order of Canada, one of the country’s highest civilian honors recognizing outstanding achievement and service.
-
E.
scikit-learn
scikit-learn is a widely used open-source Python library that provides efficient tools for data mining, data analysis, and implementing a broad range of machine learning algorithms.
- 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_69a2e79230508190b912ecb555aae17e |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2ea0dd1dc8190aecd5afdeb2fd74b |
completed | Feb. 28, 2026, 1:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3aba2b38c8190b841014b24e14822 |
completed | March 1, 2026, 2:59 a.m. |
Created at: Feb. 28, 2026, 1:06 p.m.