Triple
T3715661
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SeaWorld Orlando |
E81523
|
entity |
| Predicate | hasRollerCoaster |
P23566
|
FINISHED |
| Object | Mako |
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 | Statement: [SeaWorld Orlando, hasRollerCoaster, Mako]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mako Context triple: [SeaWorld Orlando, hasRollerCoaster, Mako]
-
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
chosen
Mako is a high-speed steel roller coaster at SeaWorld Orlando themed around the ocean’s fastest shark.
-
D.
Samu
Samu is a given name, commonly used as a short form or variant of Samuel in various cultures.
-
E.
Kai
Kai is the fictional half-Japanese, half-English outcast and skilled warrior portrayed by Keanu Reeves in the fantasy samurai film "47 Ronin."
- 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_69ad8b1a81588190b3f27a5483bb610e |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adc9cf77dc819098979094172d82d1 |
completed | March 8, 2026, 7:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4db0ba70481908dfd11ee5c89faff |
completed | March 14, 2026, 3:50 a.m. |
Created at: March 8, 2026, 3:33 p.m.