Triple
T6252337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Garden City USD 457 |
E140075
|
entity |
| Predicate | hasAbbreviation |
P43
|
FINISHED |
| Object |
USD 457
USD 457 is a public school district serving the Garden City, Kansas area.
|
E579648
|
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: USD 457 | Statement: [Garden City USD 457, hasAbbreviation, USD 457]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: USD 457 Context triple: [Garden City USD 457, hasAbbreviation, USD 457]
-
A.
USD 388
USD 388 is a public school district serving the Ellis, Kansas area, providing K–12 education to local students.
-
B.
USD 320
USD 320 is a public school district serving the Wamego, Kansas area, providing K–12 education to local students.
-
C.
USD 500
USD 500 is the unified school district serving Kansas City, Kansas, operating the public schools in that community.
-
D.
USD
USD (Universal Scene Description) is an open-source 3D scene description and interchange framework developed by Pixar, widely used for creating, composing, and collaborating on complex virtual worlds and assets.
-
E.
USD
USD is a public research university located in Vermillion, South Dakota, known for its programs in law, medicine, and business.
- 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: USD 457 Triple: [Garden City USD 457, hasAbbreviation, USD 457]
Generated description
USD 457 is a public school district serving the Garden City, Kansas area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: USD 457 Target entity description: USD 457 is a public school district serving the Garden City, Kansas area.
-
A.
USD 388
USD 388 is a public school district serving the Ellis, Kansas area, providing K–12 education to local students.
-
B.
USD 320
USD 320 is a public school district serving the Wamego, Kansas area, providing K–12 education to local students.
-
C.
USD 500
USD 500 is the unified school district serving Kansas City, Kansas, operating the public schools in that community.
-
D.
USD
USD (Universal Scene Description) is an open-source 3D scene description and interchange framework developed by Pixar, widely used for creating, composing, and collaborating on complex virtual worlds and assets.
-
E.
USD
USD is a public research university located in Vermillion, South Dakota, known for its programs in law, medicine, and business.
- 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_69c008b4858c819095b0199114a9a87b |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c063417c8c8190945049881819d307 |
completed | March 22, 2026, 9:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c244240b448190be3645177194ced6 |
completed | March 24, 2026, 7:58 a.m. |
| NEDg | Description generation | batch_69c2756cf5c88190aed2c5e0916082e7 |
completed | March 24, 2026, 11:28 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c275d913cc8190be3770f12c271226 |
completed | March 24, 2026, 11:30 a.m. |
Created at: March 22, 2026, 4:24 p.m.