Triple
T14397621
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Short Circuit |
E356989
|
entity |
| Predicate | robotName |
P50772
|
FINISHED |
| Object | Number 5 |
E1096639
|
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: Number 5 | Statement: [Short Circuit, robotName, Number 5]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Number 5 Context triple: [Short Circuit, robotName, Number 5]
-
A.
Number 5
chosen
Number 5, also known as Johnny 5, is the sentient military robot protagonist of the "Short Circuit" science fiction comedy films.
-
B.
Number 2
Number 2 is a key villainous executive and Dr. Evil’s second-in-command in the Austin Powers film series, overseeing the legitimate business front for their criminal empire.
-
C.
Five
Five was the original on-air brand name of the UK terrestrial television channel now known as Channel 5.
-
D.
Five
Five is a fan-favorite Zombies map in Call of Duty: Black Ops set in the Pentagon, featuring Cold War-era leaders battling the undead in a tight, trap-filled layout.
-
E.
Number 9
"Number 9" is a book by Sri Lankan-British designer and engineer Cecil Balmond that explores the interplay of mathematics, art, and architecture through the lens of numerical patterns and structures.
- 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_69d827927c988190ad98bb0360981783 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de90826f908190b3969af9b7cf922f |
completed | April 14, 2026, 7:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd5bc424f88190ab3a1c1aec61cb40 |
completed | May 8, 2026, 3:43 a.m. |
Created at: April 10, 2026, 1:17 a.m.