Triple
T10868
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Library of Congress |
E221
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object |
LOC
LOC is the commonly used abbreviation for the Library of Congress, the national library of the United States and one of the largest libraries in the world.
|
E1356
|
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: LOC | Statement: [Library of Congress, abbreviation, LOC]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LOC Context triple: [Library of Congress, abbreviation, LOC]
-
A.
Lee
Lee is a given name shared by numerous individuals across different cultures and professions.
-
B.
Lynn
Lynn is a coastal city in northeastern Massachusetts, known as one of the larger urban centers in the Greater Boston metropolitan area.
-
C.
de Forest
de Forest is a surname most notably associated with Lee de Forest, an American inventor and early pioneer of radio and electronic communication.
-
D.
NLS
NLS (oN-Line System) was an early, pioneering computer system that introduced many foundational concepts of modern computing, including the mouse, hypertext, and collaborative editing.
-
E.
Douglas
Douglas is a masculine given name of Scottish origin that has been widely used in English-speaking countries.
- 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: LOC Triple: [Library of Congress, abbreviation, LOC]
Generated description
LOC is the commonly used abbreviation for the Library of Congress, the national library of the United States and one of the largest libraries in the world.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: LOC Target entity description: LOC is the commonly used abbreviation for the Library of Congress, the national library of the United States and one of the largest libraries in the world.
-
A.
Lee
Lee is a given name shared by numerous individuals across different cultures and professions.
-
B.
Lynn
Lynn is a coastal city in northeastern Massachusetts, known as one of the larger urban centers in the Greater Boston metropolitan area.
-
C.
de Forest
de Forest is a surname most notably associated with Lee de Forest, an American inventor and early pioneer of radio and electronic communication.
-
D.
NLS
NLS (oN-Line System) was an early, pioneering computer system that introduced many foundational concepts of modern computing, including the mouse, hypertext, and collaborative editing.
-
E.
Douglas
Douglas is a masculine given name of Scottish origin that has been widely used in English-speaking countries.
- 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_69a23d7ad88c8190bffe8ab091d86642 |
completed | Feb. 28, 2026, 12:57 a.m. |
| NER | Named-entity recognition | batch_69a23ff415ec819082ba80ed3859b71e |
completed | Feb. 28, 2026, 1:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a248e3cd308190be7f51f817d4e2b2 |
completed | Feb. 28, 2026, 1:46 a.m. |
| NEDg | Description generation | batch_69a24b1994608190a9bd8125e52f05a3 |
completed | Feb. 28, 2026, 1:55 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a24b7f1f9c8190b93a72a7bba81eb1 |
completed | Feb. 28, 2026, 1:57 a.m. |
Created at: Feb. 28, 2026, 1:02 a.m.