Triple
T5602553
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ferris Wheel |
E147153
|
entity |
| Predicate | firstMajorExample |
P58835
|
FINISHED |
| Object | original Chicago Ferris Wheel |
—
|
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: original Chicago Ferris Wheel | Statement: [Ferris Wheel, firstMajorExample, original Chicago Ferris Wheel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstMajorExample Context triple: [Ferris Wheel, firstMajorExample, original Chicago Ferris Wheel]
-
A.
majorExample
chosen
Indicates that one entity serves as a primary or most significant example or instance of another entity.
-
B.
firstMajorPublication
Indicates the relationship where a work is the earliest significant publication associated with an entity (such as a person or organization).
-
C.
firstMajorVersionBy
Indicates that one entity is the earliest or initial major version created or released by another entity.
-
D.
firstMajorStoryArc
Indicates that the related entity represents the initial or earliest major story arc associated with another narrative work or series.
-
E.
firstMajorUseConflict
Indicates the earliest significant conflict or dispute in which the entity was prominently used or involved.
- 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_69c009043d648190a7af89698ccf1e3e |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c020dbd6dc8190ba011876c205754e |
completed | March 22, 2026, 5:03 p.m. |
| PD | Predicate disambiguation | batch_69c01b1890ec8190b9e6fa488792e4d4 |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:39 p.m.