Triple
T26374876
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Negro Southern League |
E660867
|
entity |
| Predicate | teamDemographic |
P7875
|
FINISHED |
| Object | African American teams |
—
|
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: African American teams | Statement: [Negro Southern League, teamDemographic, African American teams]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: teamDemographic Context triple: [Negro Southern League, teamDemographic, African American teams]
-
A.
teamDistribution
Indicates how members or resources are allocated or spread across different teams.
-
B.
demographicsIn
Indicates that demographic information is associated with or applies within a specified geographic or organizational area.
-
C.
hasDemographic
chosen
Indicates that an entity is associated with or characterized by a particular demographic group or attribute.
-
D.
shareDemographicFeature
Indicates that two or more entities have at least one demographic characteristic in common (such as age group, gender, ethnicity, or similar attributes).
-
E.
demographics
Indicates the relationship of providing or characterizing statistical information about a population’s attributes, such as age, gender, income, or education.
- 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_69ee812a698881908d6a58265995fa39 |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69f6106e60e48190829fa2ab14bcbbdf |
completed | May 2, 2026, 2:55 p.m. |
| PD | Predicate disambiguation | batch_69f5f800fa9c8190aab0962669fde8ac |
completed | May 2, 2026, 1:11 p.m. |
Created at: April 26, 2026, 11 p.m.