Triple
T3823302
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elmer's |
E88625
|
entity |
| Predicate | hasBrandVariant |
P13477
|
FINISHED |
| Object | Elmer's Foam Board |
E88625
|
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: Elmer's Foam Board | Statement: [Elmer's, hasBrandVariant, Elmer's Foam Board]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elmer's Foam Board Context triple: [Elmer's, hasBrandVariant, Elmer's Foam Board]
-
A.
Mylar
Mylar is a durable, transparent polyester film widely used for packaging, insulation, and protective applications.
-
B.
Elmer's
chosen
Elmer's is a well-known American brand best recognized for its school and craft glues and other adhesive products.
-
C.
Play-Doh
Play-Doh is a colorful modeling compound primarily used by children for arts and crafts, known for its soft, malleable texture and distinctive smell.
-
D.
Viewliner
Viewliner is a class of single-level passenger railcars used primarily by Amtrak for long-distance overnight service in the United States.
-
E.
Linoleum
Linoleum is a 2022 indie sci-fi dramedy film in which Jim Gaffigan plays a failing children’s TV host who begins experiencing surreal events that blur the line between reality and fantasy.
- 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_69aed9538cf881909d9ce8ca4ac7c18c |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeea63fe2c8190825f6e9451f6aa50 |
completed | March 9, 2026, 3:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4fb4c70008190bb8712f46f40d6f8 |
completed | March 14, 2026, 6:08 a.m. |
Created at: March 9, 2026, 3:17 p.m.