Triple
T9788945
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bailey |
E237557
|
entity |
| Predicate | friendOf |
P8712
|
FINISHED |
| Object | Dory |
E62340
|
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: Dory | Statement: [Bailey, friendOf, Dory]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dory Context triple: [Bailey, friendOf, Dory]
-
A.
Nemo
Nemo is a graphical file manager for the Cinnamon desktop environment, known for its user-friendly interface and integration with Linux Mint.
-
B.
Nemo
Nemo is the young, adventurous clownfish protagonist of Pixar's animated film "Finding Nemo."
-
C.
Nemo
Nemo is the Latin word for "no one" or "nobody," often used as a name in literature and popular culture, most famously for Captain Nemo in Jules Verne’s novels.
-
D.
Dory in Finding Dory
chosen
Dory in Finding Dory is a friendly, optimistic blue tang fish with short-term memory loss who embarks on an ocean-spanning journey to find her long-lost family.
-
E.
Bailey in Finding Dory
Bailey in Finding Dory is a neurotic but kind-hearted beluga whale at the Marine Life Institute who uses echolocation to help Dory and her friends on their journey.
- 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_69ca84da927881909bda80caecad6010 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda214875481909f39e1d4dbac1fdb |
completed | April 1, 2026, 10:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d20d1b170881908954233f927457a0 |
completed | April 5, 2026, 7:19 a.m. |
Created at: March 30, 2026, 8:27 p.m.