Triple
T796661
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Burj Al Arab |
E17036
|
entity |
| Predicate | hasUnderwaterThemedRestaurant |
P19138
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Burj Al Arab, hasUnderwaterThemedRestaurant, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUnderwaterThemedRestaurant Context triple: [Burj Al Arab, hasUnderwaterThemedRestaurant, yes]
-
A.
hasRestaurant
Indicates that one entity possesses, operates, or contains a restaurant associated with it.
-
B.
hasPier
Indicates that a location or structure possesses or includes a pier as part of its features.
-
C.
hasWaterPark
Indicates that one entity possesses, includes, or features a water park as part of its facilities or attributes.
-
D.
containsAttraction
Indicates that one entity includes or encompasses an attraction (such as a point of interest, feature, or draw) within its bounds or scope.
-
E.
hasChampagneBar
Indicates that an entity includes, features, or is equipped with a champagne bar.
- 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_69a49378b9c48190adbf5f62e5b7aca1 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a7b172e88190a26d31c9075b81fb |
completed | March 1, 2026, 8:55 p.m. |
| PD | Predicate disambiguation | batch_69a4a5122a008190b0c621b7bc588d41 |
completed | March 1, 2026, 8:44 p.m. |
| PDg | Predicate description generation | batch_69a4a5bed20c81909ecc28bf42594e72 |
completed | March 1, 2026, 8:46 p.m. |
Created at: March 1, 2026, 7:38 p.m.