Triple
T15784348
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mako |
E382697
|
entity |
| Predicate | themedAfter |
P5290
|
FINISHED |
| Object | mako shark |
E382697
|
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: mako shark | Statement: [Mako, themedAfter, mako shark]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: mako shark Context triple: [Mako, themedAfter, mako shark]
-
A.
Mako
Mako was a Japanese-American actor and voice actor known for his distinctive voice and roles in films like "Conan the Barbarian" and as the voice of Iroh in "Avatar: The Last Airbender."
-
B.
Mako
Mako is a Japanese imperial family member best known as Princess Mako of Akishino, the former princess who left royal status upon her marriage to a commoner.
-
C.
Mako
Mako is the nickname of Benjamin Mako Hill, a prominent free software activist, scholar, and developer involved with projects like Debian and Wikimedia.
-
D.
Mako
chosen
Mako is a high-speed steel roller coaster at SeaWorld Orlando themed around the ocean’s fastest shark.
-
E.
Mako
Mako is a central firebending protagonist in *The Legend of Korra*, known for his serious demeanor, leadership in Team Avatar, and complex romantic relationships.
- 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_69d86da16e188190b89af699f1ed0bfe |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e05401c4788190a31c180953433db9 |
completed | April 16, 2026, 3:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff90a4661481909d04bcb9f5043a6b |
completed | May 9, 2026, 7:53 p.m. |
Created at: April 10, 2026, 4:48 a.m.