Triple
T7751317
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SAN |
E175768
|
entity |
| Predicate | hasFAAIdentifier |
P420
|
FINISHED |
| Object | SAN |
E32609
|
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: SAN | Statement: [SAN, hasFAAIdentifier, SAN]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SAN Context triple: [SAN, hasFAAIdentifier, SAN]
-
A.
SAN
chosen
SAN is the three-letter IATA airport code for San Diego International Airport, the primary commercial airport serving the San Diego, California area.
-
B.
SAN
SAN is the acronym for the Schuylkill Action Network, a collaborative partnership focused on protecting and restoring the Schuylkill River watershed.
-
C.
SAN
SAN is the stock ticker symbol for Banco Santander S.A., a major Spanish multinational banking and financial services company.
-
D.
SAM
SAM is the official FIFA trigramme used to represent the Samoa national under-20 football team in international competitions and records.
-
E.
SAM
SAM is the commonly used abbreviation for the South Australian Museum, a major natural history and cultural institution located in Adelaide, Australia.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c69960b3588190a53aa590d31d9544 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c703b264c0819095c37534a676531d |
completed | March 27, 2026, 10:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8be576274819092e5ebdbcf2361da |
completed | March 29, 2026, 5:53 a.m. |
Created at: March 27, 2026, 4:08 p.m.