Triple
T13780990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ORF |
E331129
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object | ORF |
E331129
|
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: ORF | Statement: [ORF, abbreviation, ORF]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ORF Context triple: [ORF, abbreviation, ORF]
-
A.
ORF
chosen
ORF is Austria's national public service broadcaster, operating multiple television and radio channels as well as online services.
-
B.
ORF
ORF is the three-letter IATA airport code for Norfolk International Airport in Norfolk, Virginia.
-
C.
ORU
ORU is a private Christian liberal arts university in Tulsa, Oklahoma, founded by evangelist Oral Roberts and known for integrating faith-based education with a diverse range of academic programs.
-
D.
ORU
ORU is the IATA airport code for Juan Mendoza Airport, a regional airport serving the city of Oruro in Bolivia.
-
E.
ORC
ORC (Optimized Row Columnar) is a highly efficient, columnar storage file format commonly used in big data systems to enable fast analytics and compression.
- 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_69d81c583b0081909e408a17db517a21 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de02460a688190a27874f8d35819c7 |
completed | April 14, 2026, 9 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7b079013881908e9f5412e5dfb0b2 |
completed | May 3, 2026, 8:30 p.m. |
Created at: April 9, 2026, 10:11 p.m.