Triple
T3610857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christina Unkel |
E76481
|
entity |
| Predicate | primarySportContext |
P1080
|
FINISHED |
| Object | women's soccer |
—
|
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: women's soccer | Statement: [Christina Unkel, primarySportContext, women's soccer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primarySportContext Context triple: [Christina Unkel, primarySportContext, women's soccer]
-
A.
primarySport
chosen
Indicates the main sport with which an entity (such as a person, team, or organization) is most closely associated or primarily involved.
-
B.
nationalSportContext
Indicates that a particular sport is recognized or designated as the national sport of a given country or region within a specific contextual framework.
-
C.
primaryArenaSport
Indicates the main sport that is primarily played or hosted in a given arena.
-
D.
originalSport
Indicates that one sport is the initial or primary sport associated with an entity, often before any change, adaptation, or transition to another sport.
-
E.
popularSport
Indicates that a sport is widely liked, followed, or played by many people within a certain group or region.
- 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_69ad85da0ba481908b3b48c69efe2b98 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc22cac3c8190bc5f7c45d31668c1 |
completed | March 8, 2026, 6:38 p.m. |
| PD | Predicate disambiguation | batch_69adb83d8b1c8190b3bddbc5dc995a87 |
completed | March 8, 2026, 5:56 p.m. |
Created at: March 8, 2026, 3:23 p.m.