Triple
T10605668
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hormel Foods Corporation |
E275869
|
entity |
| Predicate | hasBrand |
P1500
|
FINISHED |
| Object | SPAM |
E624468
|
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: SPAM | Statement: [Hormel Foods Corporation, hasBrand, SPAM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SPAM Context triple: [Hormel Foods Corporation, hasBrand, SPAM]
-
A.
Spam
chosen
Spam is a canned, precooked meat product made primarily from pork and ham, widely known for its long shelf life and use in various dishes around the world.
-
B.
The Junk Mail
"The Junk Mail" is an episode of the television sitcom Seinfeld, notable for its storyline involving Kramer’s battle against postal junk mail and David Puddy’s recurring role.
-
C.
Spam sketch
The "Spam" sketch is a famous Monty Python comedy routine in which a café's menu is comically dominated by Spam, leading to increasingly absurd repetition of the word.
-
D.
sns
sns is the conventional alias used when importing Seaborn, a popular Python data visualization library built on top of Matplotlib.
-
E.
ADS
ADS is the three-letter IATA airport code for Addison Airport, a general aviation airport serving the Dallas–Fort Worth area in Texas, United States.
- 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_69d6aaf948d88190806cc3a8c47a3fb2 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d6df4a5df88190b993196ca7849a88 |
completed | April 8, 2026, 11:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d95eb726bc8190a8db7357bd126016 |
completed | April 10, 2026, 8:33 p.m. |
Created at: April 8, 2026, 7:32 p.m.