Triple
T8831034
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Erie Monsters |
E210140
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object | LEM |
E254493
|
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: LEM | Statement: [Lake Erie Monsters, abbreviation, LEM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LEM Context triple: [Lake Erie Monsters, abbreviation, LEM]
-
A.
LEM
chosen
LEM is the original abbreviation for the Apollo Lunar Module, the spacecraft used by NASA astronauts to land on and ascend from the Moon during the Apollo missions.
-
B.
LER
LER is the vehicle registration code assigned to the German island municipality of Borkum.
-
C.
Le
Le is a common Vietnamese surname shared by many notable figures in the country’s history and culture.
-
D.
LM
LM is the Apollo Lunar Module, the spacecraft used by NASA during the Apollo program to land astronauts on the Moon and return them to lunar orbit.
-
E.
LM
LM is the vehicle registration code used for cars registered in the town of Liptovský Mikuláš in Slovakia.
- 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_69ca8365b28081909e48e45e95dfc405 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc604ed2b88190b4f53b34b5a438f7 |
completed | April 1, 2026, 12:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf896cf5a8819098a76288bd505c1e |
completed | April 3, 2026, 9:33 a.m. |
Created at: March 30, 2026, 6:47 p.m.