Triple
T14201942
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tuskegee syphilis study |
E351984
|
entity |
| Predicate | numberOfControls |
P42152
|
FINISHED |
| Object | approximately 201 men without syphilis |
—
|
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 201 men without syphilis | Statement: [Tuskegee syphilis study, numberOfControls, approximately 201 men without syphilis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfControls Context triple: [Tuskegee syphilis study, numberOfControls, approximately 201 men without syphilis]
-
A.
controlsWith
Indicates that one entity exercises authority, direction, or regulatory power over another entity through specific means or mechanisms.
-
B.
numberOfCounts
chosen
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
-
C.
languagePanelCount
Indicates the number of language panels associated with or present in a given context or entity.
-
D.
numberOfModels
Indicates the quantity or count of models associated with a given entity or context.
-
E.
numberOfConfigurations
Indicates the total count of distinct configurations associated with or applicable to a given entity or situation.
- 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_69d827894ac0819097803e57f3227b23 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61f589a08190b71ad4e69d92ffd0 |
completed | April 14, 2026, 3:49 p.m. |
| PD | Predicate disambiguation | batch_69de05bcd7d48190a4848d9320404aa6 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 10, 2026, 1:05 a.m.