Triple
T9404897
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boston Dynamics |
E226560
|
entity |
| Predicate | developed |
P73
|
FINISHED |
| Object | Spot |
E795932
|
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: [Boston Dynamics, developed, Spot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Spot Context triple: [Boston Dynamics, developed, Spot]
-
A.
Spot
chosen
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.
Kogo
Kogo is a settlement located in the Litoral region of Equatorial Guinea.
-
C.
Elke
Elke is a feminine given name of German origin commonly used in German-speaking countries.
-
D.
Kiko
Kiko is the young, albino giant ape who serves as the gentle offspring and companion of King Kong in the 1933 film "Son of Kong."
-
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_69ca843170f88190800a8ab2b5fc568e |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd51c125dc8190a6438cf0ee23e7a9 |
completed | April 1, 2026, 5:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1079bd644819081f9c8f25ff1c532 |
completed | April 4, 2026, 12:44 p.m. |
Created at: March 30, 2026, 7:46 p.m.