Triple
T16924459
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fitz Eugene Dixon Education Building |
E410530
|
entity |
| Predicate | primaryLanguageOfPrograms |
P124765
|
FINISHED |
| Object | English |
—
|
LITERAL 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: English | Statement: [Fitz Eugene Dixon Education Building, primaryLanguageOfPrograms, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryLanguageOfPrograms Context triple: [Fitz Eugene Dixon Education Building, primaryLanguageOfPrograms, English]
-
A.
languageOfPrimaryProgramming
Indicates the programming language that is primarily used to implement or develop a given entity.
-
B.
primaryProgramming
Indicates that one entity serves as the main or most important programming or coding language, tool, or environment used by another entity.
-
C.
programLanguage
Indicates that an entity is implemented, written, or expressed using a particular programming language.
-
D.
languageOfProgramming
Indicates that one entity is a programming language used to implement, develop, or script the other entity.
-
E.
languageOfPrimaryCompilation
Indicates the programming or source language in which an entity was primarily compiled.
- F. None of above. chosen
Provenance (4 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_69d886c7b1e481908c3766dfa8c13458 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3cdf1347c819085cf754a0c3e19ed |
completed | April 18, 2026, 6:31 p.m. |
| PD | Predicate disambiguation | batch_69e32b982f548190b08414d55810de19 |
completed | April 18, 2026, 6:58 a.m. |
| PDg | Predicate description generation | batch_69e32d7aae948190bc238d765795688c |
completed | April 18, 2026, 7:06 a.m. |
Created at: April 10, 2026, 5:30 a.m.