Triple
T21355947
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boulevard de Grenelle |
E526623
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Eiffel Tower |
—
|
NE NERFINISHED |
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: Eiffel Tower | Statement: [Boulevard de Grenelle, near, Eiffel Tower]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eiffel Tower Context triple: [Boulevard de Grenelle, near, Eiffel Tower]
-
A.
Eiffel Tower
chosen
The Eiffel Tower is a wrought-iron lattice tower in Paris, France, and one of the most recognizable landmarks and symbols of the country.
-
B.
Eiffel
Eiffel is a French surname most famously associated with engineer Gustave Eiffel, designer of the Eiffel Tower in Paris.
-
C.
Eiffel
Eiffel is an object-oriented programming language designed by Bertrand Meyer, known for its emphasis on software correctness through the Design by Contract methodology.
-
D.
Iron Tower
The Iron Tower is a historic medieval watchtower and former city gate located in Mainz, Germany.
-
E.
58 Tour Eiffel
58 Tour Eiffel is a contemporary French restaurant located on the first floor of the Eiffel Tower, offering panoramic views of Paris.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b51d8a308190b09113b3b3f9bc15 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8af9cd4a48190aa6e72c019b60f8c |
completed | April 22, 2026, 11:23 a.m. |
Created at: April 16, 2026, 5:06 p.m.