Triple
T15625110
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Good Dinosaur |
E375658
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object | Spot |
E1167754
|
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: Spot | Statement: [The Good Dinosaur, character, Spot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Spot Context triple: [The Good Dinosaur, character, Spot]
-
A.
Spot
Spot is a four-legged, dog-like robotic platform developed by Boston Dynamics, known for its agility, autonomy, and use in industrial and inspection tasks.
-
B.
Spot
chosen
Spot is the feral human boy who befriends the young dinosaur Arlo in Pixar's animated film "The Good Dinosaur."
-
C.
Kogo
Kogo is a settlement located in the Litoral region of Equatorial Guinea.
-
D.
Elke
Elke is a feminine given name of German origin commonly used in German-speaking countries.
-
E.
Kiko
Kiko is the Crown Princess of Japan and the wife of Crown Prince Akishino, a prominent member of the Japanese imperial family.
- 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_69d85cd035a48190b73d5579ab73969a |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e9e5e248190ae54cda1fde51efb |
completed | April 16, 2026, 2:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff678c9d8c8190be73b6e7ed558e99 |
completed | May 9, 2026, 4:57 p.m. |
Created at: April 10, 2026, 4:14 a.m.