Triple
T8248402
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hydra |
E192900
|
entity |
| Predicate | supportsProtocol |
P203
|
FINISHED |
| Object | AFP |
E193824
|
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: AFP | Statement: [Hydra, supportsProtocol, AFP]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AFP Context triple: [Hydra, supportsProtocol, AFP]
-
A.
AFP
AFP is a major international news agency based in Paris that provides global news coverage in multiple languages.
-
B.
AFP
chosen
AFP (Apple Filing Protocol) is a network protocol developed by Apple for file services and sharing primarily on macOS and classic Mac OS systems.
-
C.
AFP
AFP is the unified military organization responsible for the defense and security of the Republic of the Philippines.
-
D.
AFP
AFP is the national law enforcement agency of Australia responsible for federal policing, counter-terrorism, and protecting Commonwealth interests domestically and overseas.
-
E.
ADF
ADF is the concessional financing arm of the African Development Bank Group that provides low-interest loans and grants to support economic and social development in Africa’s poorest countries.
- 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_69ca82de7b8c81908d8106f8a53cff9b |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb78c6b3c48190a3ecebf449766124 |
completed | March 31, 2026, 7:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd3530ca148190a28761622d0cf663 |
completed | April 1, 2026, 3:09 p.m. |
Created at: March 30, 2026, 5:48 p.m.