Triple
T15908702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christopher Guest |
E385789
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Mascots |
E796590
|
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: Mascots | Statement: [Christopher Guest, notableWork, Mascots]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mascots Context triple: [Christopher Guest, notableWork, Mascots]
-
A.
Mascots
chosen
Mascots is a 2016 mockumentary comedy film directed by Christopher Guest that follows the quirky competitors in a global mascot competition.
-
B.
Mascot
Mascot is an inner-southern suburb of Sydney, Australia, best known for being home to the city's major international airport.
-
C.
Mascot
Mascot is a small unincorporated community in eastern Knox County, Tennessee, known historically for its ties to mining and quarry operations.
-
D.
MASCOT
MASCOT is a small German-French-built lander that accompanied Japan’s Hayabusa2 mission to explore and study the surface of asteroid Ryugu.
-
E.
Olympic Games mascots
Olympic Games mascots are specially designed characters that personify the spirit, culture, and themes of each edition of the Olympic Games and are used for promotion, fan engagement, and merchandising.
- 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_69d86da686e4819097cbf3b1fc2d881d |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1565d2f048190a40379ceae00411a |
completed | April 16, 2026, 9:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffb055307081908a13c98a0e16780c |
completed | May 9, 2026, 10:08 p.m. |
Created at: April 10, 2026, 4:52 a.m.