Triple
T6620410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santa Monica Airport |
E149658
|
entity |
| Predicate | FAAcode |
P420
|
FINISHED |
| Object | SMO |
E601157
|
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: SMO | Statement: [Santa Monica Airport, FAAcode, SMO]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SMO Context triple: [Santa Monica Airport, FAAcode, SMO]
-
A.
SMO
chosen
SMO is the IATA airport code for Santa Monica Airport, a general aviation facility located in Santa Monica, California.
-
B.
SMI
SMI is the vehicle registration code assigned to the town of Mikołów in Poland.
-
C.
SÖM
SÖM is the vehicle registration code for the Sömmerda district in the German state of Thuringia.
-
D.
SMK
SMK is the commonly used abbreviation for the Office of the Prime Minister of Norway, the central executive body that supports the Norwegian Prime Minister and coordinates government policy.
-
E.
Sma
Sma is a classic rabbinic commentator best known for his influential glosses on the Choshen Mishpat section of the Shulchan Aruch, widely studied in Jewish law.
- 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_69c687ed8a9c81908bb671717cb192ef |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6af7aff44819089da6145e1ef5f76 |
completed | March 27, 2026, 4:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6e4461e748190b4feead6ef16a01c |
completed | March 27, 2026, 8:10 p.m. |
Created at: March 27, 2026, 1:58 p.m.