Triple
T67815
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eiffel Tower |
E1351
|
entity |
| Predicate | cityPanorama |
P4219
|
FINISHED |
| Object | Paris skyline |
—
|
LITERAL 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: Paris skyline | Statement: [Eiffel Tower, cityPanorama, Paris skyline]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cityPanorama Context triple: [Eiffel Tower, cityPanorama, Paris skyline]
-
A.
city2
Indicates a relationship where one entity is identified as a city associated with, located in, or otherwise linked to another entity.
-
B.
city1
Indicates that the subject is classified as a city.
-
C.
museumCity
Indicates the city in which a given museum is located.
-
D.
streetNetwork
Indicates the layout and connectivity relationships among streets within a geographic area, including how roads intersect, link, and form a navigable network.
-
E.
notableCityCommunity
Indicates a relationship where a city is notably associated with, recognized by, or significantly connected to a particular community.
- F. None of above. chosen
Provenance (4 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_69a24c06b3bc8190aa4ac89026115efc |
completed | Feb. 28, 2026, 1:59 a.m. |
| NER | Named-entity recognition | batch_69a2509b5a088190bb9d2b650aeb8bca |
completed | Feb. 28, 2026, 2:19 a.m. |
| PD | Predicate disambiguation | batch_69a24ea749788190bc17865171ff909a |
completed | Feb. 28, 2026, 2:10 a.m. |
| PDg | Predicate description generation | batch_69a2509a1c088190b4afa3045455709a |
completed | Feb. 28, 2026, 2:19 a.m. |
Created at: Feb. 28, 2026, 2:03 a.m.