Triple
T1339389
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pan American Games |
E28429
|
entity |
| Predicate | typicalNumberOfAthletes |
P27342
|
FINISHED |
| Object | several thousand |
—
|
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: several thousand | Statement: [Pan American Games, typicalNumberOfAthletes, several thousand]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalNumberOfAthletes Context triple: [Pan American Games, typicalNumberOfAthletes, several thousand]
-
A.
numberOfMaleAthletes
Indicates the quantity of athletes in a given group or context who are male.
-
B.
hasNumberOfStudentAthletes
Indicates the relationship that specifies how many student athletes are associated with a given entity.
-
C.
numberOfFemaleAthletes
Indicates the count of athletes who are female in a given context or group.
-
D.
typicalTeamSize
Indicates the usual or most common number of members that make up a given team.
-
E.
numberOfSports
Indicates the quantity of distinct sports associated with or involved in a given entity.
- 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_69a49854eb3481908c7d56b2e449a290 |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c21303c881908fef0b32831222fe |
completed | March 1, 2026, 10:47 p.m. |
| PD | Predicate disambiguation | batch_69a4bef3e8fc8190ac9a1ba9b5879483 |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4c06721488190ac7f6e012f21af3d |
completed | March 1, 2026, 10:40 p.m. |
Created at: March 1, 2026, 7:56 p.m.