Triple
T4839643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eagan, Minnesota |
E108144
|
entity |
| Predicate | headquartersLocationOf |
P62
|
FINISHED |
| Object |
Scantron
Scantron is an American company best known for its machine-readable testing and assessment forms and related educational technology services.
|
E474864
|
NE FINISHED |
How this triple was built (4 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: Scantron | Statement: [Eagan, Minnesota, headquartersLocationOf, Scantron]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Scantron Context triple: [Eagan, Minnesota, headquartersLocationOf, Scantron]
-
A.
OMR
OMR is the official currency code used to represent the Omani rial, the national currency of Oman.
-
B.
OMR
OMR refers to the European Union’s outermost regions, which are geographically distant territories fully integrated into the EU and subject to its laws and policies.
-
C.
CompuMark
CompuMark is a trademark research and brand protection company that provides data and analytics services to help businesses manage and safeguard their intellectual property portfolios.
-
D.
Scanners
Scanners is a 1981 science fiction horror film directed by David Cronenberg, best known for its intense psychic warfare and iconic head-explosion scene.
-
E.
Proscan
Proscan is a consumer electronics brand known for producing affordable televisions and related audio-visual equipment.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Scantron Triple: [Eagan, Minnesota, headquartersLocationOf, Scantron]
Generated description
Scantron is an American company best known for its machine-readable testing and assessment forms and related educational technology services.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Scantron Target entity description: Scantron is an American company best known for its machine-readable testing and assessment forms and related educational technology services.
-
A.
OMR
OMR is the official currency code used to represent the Omani rial, the national currency of Oman.
-
B.
OMR
OMR refers to the European Union’s outermost regions, which are geographically distant territories fully integrated into the EU and subject to its laws and policies.
-
C.
CompuMark
CompuMark is a trademark research and brand protection company that provides data and analytics services to help businesses manage and safeguard their intellectual property portfolios.
-
D.
Scanners
Scanners is a 1981 science fiction horror film directed by David Cronenberg, best known for its intense psychic warfare and iconic head-explosion scene.
-
E.
Proscan
Proscan is a consumer electronics brand known for producing affordable televisions and related audio-visual equipment.
- F. None of above. chosen
Provenance (5 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_69bd43fbe444819085cb970706ef73f7 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6ce4a5108190aede620d5dde1f81 |
completed | March 20, 2026, 3:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be5cc5861881908ad3838168325a34 |
completed | March 21, 2026, 8:54 a.m. |
| NEDg | Description generation | batch_69be5ed104e48190b01ea97094be6a96 |
completed | March 21, 2026, 9:03 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be5fe07d708190ba0dde4c081e15c6 |
completed | March 21, 2026, 9:07 a.m. |
Created at: March 20, 2026, 1:25 p.m.