Triple
T11720635
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SFS |
E278620
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | SFS |
unclear NED1
|
NE 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: SFS | Statement: [SFS, shortName, SFS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SFS Context triple: [SFS, shortName, SFS]
-
A.
SFS
SFS is the abbreviation for the Senior Foreign Service, the elite cadre of senior-ranking career diplomats in the United States Foreign Service.
-
B.
SFS
SFS is a common abbreviation for Allianz Stadium, a major sports and entertainment venue in Sydney, Australia.
-
C.
SFS
SFS is a renowned Georgetown University school specializing in international affairs, diplomacy, and global policy education.
-
D.
SFS
SFS is the commonly used abbreviation for the San Francisco Symphony, a major American orchestra based in San Francisco, California.
-
E.
SFS
SFS is a spatial feature standard that defines how geographic features and their properties are modeled and accessed in geospatial information systems.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69d6aaff2ce88190b4a1e4b341ad5377 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a4c26e4c8190ae30d906b4fd4221 |
completed | April 10, 2026, 7:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ef83b9131c819085f7bcab902c3763 |
completed | April 27, 2026, 3:41 p.m. |
Created at: April 8, 2026, 9:40 p.m.