Triple
T3665934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CODASPY |
E77758
|
entity |
| Predicate | hasAbbreviation |
P43
|
FINISHED |
| Object | CODASPY |
E77758
|
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: CODASPY | Statement: [CODASPY, hasAbbreviation, CODASPY]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CODASPY Context triple: [CODASPY, hasAbbreviation, CODASPY]
-
A.
CODASPY
chosen
CODASPY is an academic conference focused on research in data and application security and privacy.
-
B.
Pydna
Pydna was an ancient Macedonian coastal city notable as a political and military center, especially during the era of the Macedonian kingdom and the Roman conquest of Greece.
-
C.
Pythonidae
Pythonidae is a family of nonvenomous constrictor snakes that includes pythons found across Africa, Asia, and Australia.
-
D.
M-Code
M-Code is an advanced, encrypted military GPS signal designed to provide more secure and jam-resistant positioning and navigation for authorized users.
-
E.
Vyper
Vyper is a Pythonic, security-focused programming language used to write smart contracts on the Ethereum blockchain.
- 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_69ad85dfc4dc8190a441864202ab2a7a |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc40188988190b1b7ac9c8240a5ff |
completed | March 8, 2026, 6:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4884bd50c8190a334e9aadc734364 |
completed | March 13, 2026, 9:57 p.m. |
Created at: March 8, 2026, 3:25 p.m.