Triple
T15357486
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kragle |
E367199
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | Krazy Glue |
E1048563
|
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: Krazy Glue | Statement: [Kragle, alsoKnownAs, Krazy Glue]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Krazy Glue Context triple: [Kragle, alsoKnownAs, Krazy Glue]
-
A.
Bostik
chosen
Bostik is a global adhesive and sealant manufacturer known for its products used in construction, industrial, and consumer markets.
-
B.
Glue
"Glue" is a novel by Scottish author Irvine Welsh that follows the intertwined lives of four working-class friends in Edinburgh over several decades.
-
C.
Viewliner
Viewliner is a class of single-level passenger railcars used primarily by Amtrak for long-distance overnight service in the United States.
-
D.
Bondo
Bondo is a town in western Kenya’s Nyanza region, known as an administrative and commercial center near Lake Victoria.
-
E.
Bondo
Bondo is a town located in the northeastern part of the Democratic Republic of the Congo.
- 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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e2d4934819097fc63603964217c |
completed | April 16, 2026, 1:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff0b45e3048190a7fa62ead6916fed |
completed | May 9, 2026, 10:24 a.m. |
Created at: April 10, 2026, 3:18 a.m.