Triple
T7770715
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metro Line 54 |
E179060
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Gein |
E165425
|
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: Gein | Statement: [Metro Line 54, hasStation, Gein]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gein Context triple: [Metro Line 54, hasStation, Gein]
-
A.
Gein
chosen
Gein is a metro station in Amsterdam, Netherlands, serving as one of the termini of the city's metro network.
-
B.
Geikie
Geikie is a Scottish surname most notably associated with Archibald Geikie, a prominent 19th-century geologist and director of the British Geological Survey.
-
C.
Giez
Giez is a small municipality in the canton of Vaud in western Switzerland, situated near the town of Grandson and close to Lake Neuchâtel.
-
D.
Gekū
Gekū is one of the two main shrines of the Ise Grand Shrine complex in Japan, traditionally dedicated to the Shinto deity of food, clothing, and shelter.
-
E.
Gooigi
Gooigi is a green, goo-like doppelgänger of Luigi from the Luigi’s Mansion series, used as a playable helper character to solve puzzles and reach otherwise inaccessible areas.
- 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_69c69f30602c819082ab52cd4af5c592 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c70438ca2481909114b0c434717109 |
completed | March 27, 2026, 10:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8deb3f1008190b3e64f202df421b2 |
completed | March 29, 2026, 8:11 a.m. |
Created at: March 27, 2026, 4:11 p.m.