Triple
T4315725
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dubai Millennium |
E96385
|
entity |
| Predicate | timeformRating |
P56421
|
FINISHED |
| Object | 140 |
—
|
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: 140 | Statement: [Dubai Millennium, timeformRating, 140]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timeformRating Context triple: [Dubai Millennium, timeformRating, 140]
-
A.
ratingContext
Indicates the situational or contextual factors under which a rating is given or applies.
-
B.
hasTourismRating
Indicates that an entity has been assigned a specific tourism-related quality or rating, reflecting its appeal or suitability for tourists.
-
C.
ratingDescription
Indicates the textual explanation or qualitative summary associated with a given rating or score.
-
D.
ratingSystem
Indicates a system or method used to assign evaluative scores or rankings to items, actions, or entities based on defined criteria.
-
E.
peakRating
Indicates the highest rating value that has been achieved or recorded for an entity over a given period or context.
- 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_69b345422aac81909ddbadae437d122e |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b350f60dfc819098202b7eb3bbf402 |
completed | March 12, 2026, 11:49 p.m. |
| PD | Predicate disambiguation | batch_69b34f4a07b08190a06ada0d9cbb14fb |
completed | March 12, 2026, 11:42 p.m. |
| PDg | Predicate description generation | batch_69b35034cd248190bae09e9d090e13ec |
completed | March 12, 2026, 11:45 p.m. |
Created at: March 12, 2026, 11:12 p.m.