Triple
T4678905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aiguille Verte |
E103748
|
entity |
| Predicate | photoAppearsOn |
P34220
|
FINISHED |
| Object | many classic Chamonix valley views |
—
|
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: many classic Chamonix valley views | Statement: [Aiguille Verte, photoAppearsOn, many classic Chamonix valley views]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: photoAppearsOn Context triple: [Aiguille Verte, photoAppearsOn, many classic Chamonix valley views]
-
A.
publicImage
Indicates how an entity is perceived or represented by the general public or broader audience.
-
B.
commonlyDepictedOn
chosen
Indicates that something is frequently shown or represented on the surface, medium, or context of another thing.
-
C.
mediaDepictionAs
Indicates that one entity is portrayed or represented as another entity or in a particular way within some medium (e.g., image, film, text).
-
D.
imagedIn
Indicates that one entity appears within or is depicted in an image associated with another entity.
-
E.
famousImage
Indicates that an image is widely recognized or well-known, typically due to its prominence, popularity, or cultural significance.
- F. None of above.
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_69bd43dda32c8190938b37744ca270fc |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd67c9c3c08190a6c4944cdd1362a8 |
completed | March 20, 2026, 3:29 p.m. |
| PD | Predicate disambiguation | batch_69bd6217e0088190836570522e324dc6 |
completed | March 20, 2026, 3:04 p.m. |
Created at: March 20, 2026, 1:16 p.m.