Triple
T7694473
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Myers–Briggs Type Indicator |
E174334
|
entity |
| Predicate | hasQuestionCountRange |
P15109
|
FINISHED |
| Object | approximately 90–100 items (depending on form) |
—
|
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: approximately 90–100 items (depending on form) | Statement: [Myers–Briggs Type Indicator, hasQuestionCountRange, approximately 90–100 items (depending on form)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasQuestionCountRange Context triple: [Myers–Briggs Type Indicator, hasQuestionCountRange, approximately 90–100 items (depending on form)]
-
A.
numberOfQuestions
chosen
Indicates the total count of questions associated with or contained in a given entity or context.
-
B.
numberOfQueries
Indicates the total count of queries associated with or performed in a given context or entity.
-
C.
hasRange
Indicates that a property or relation is constrained to take its values from a specified class, type, or value set.
-
D.
hasEnrollmentRange
Indicates that there is a specified minimum and/or maximum number of participants allowed or expected for an enrollment in a given context.
-
E.
hasOpenQuestions
Indicates that there are unresolved or unanswered issues, problems, or inquiries associated with the referenced entity or context.
- F. None of above.
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_69c6995966348190939e6c37ba272c06 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c706d1f0208190bc5b695aa5736244 |
completed | March 27, 2026, 10:38 p.m. |
| PD | Predicate disambiguation | batch_69c70163dea88190ae729df50e63dfd7 |
completed | March 27, 2026, 10:15 p.m. |
Created at: March 27, 2026, 4:02 p.m.