Triple
T35097481
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Minimundus |
E1012904
|
entity |
| Predicate | featuresModelOf |
P104084
|
FINISHED |
| Object | Eiffel Tower |
E1351
|
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: Eiffel Tower | Statement: [Minimundus, featuresModelOf, Eiffel Tower]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresModelOf Context triple: [Minimundus, featuresModelOf, Eiffel Tower]
-
A.
featuresModel
chosen
Indicates that one entity includes, exposes, or is characterized by a particular model as one of its defining components or capabilities.
-
B.
deviceModel
Indicates the specific model designation associated with a device.
-
C.
platformModel
Indicates that one entity serves as the underlying platform or foundational model upon which the other entity is built, operates, or depends.
-
D.
featuresMode
Indicates that one entity operates in, supports, or is characterized by a particular mode or configuration specified by another entity.
-
E.
brandFeatures
Indicates that a brand includes, offers, or is characterized by a particular feature or attribute.
- F. None of above.
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_69f76dd556248190808b4c4f43debebb |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a037c8c34f88190ace26f555827f23e |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a37cfe932b881908565510edd8830c4 |
completed | June 21, 2026, 11:50 a.m. |
| PD | Predicate disambiguation | batch_6a037a016960819093ed4990fb4d9d36 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:01 p.m.