Triple
T23045612
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hotel of Doom |
E573868
|
entity |
| Predicate | appliedToStructureType |
P106537
|
FINISHED |
| Object | hotel skyscraper |
—
|
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: hotel skyscraper | Statement: [Hotel of Doom, appliedToStructureType, hotel skyscraper]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appliedToStructureType Context triple: [Hotel of Doom, appliedToStructureType, hotel skyscraper]
-
A.
appliesToPropertyType
Indicates that something (such as a rule, constraint, or operation) is relevant to or valid for a specific type of property.
-
B.
hasStructureType
Indicates that an entity possesses or is classified by a specific structural type or configuration.
-
C.
appliesToSegmentType
Indicates that a rule, condition, or operation is specifically associated with and relevant to a particular type of segment.
-
D.
appliedToGroupType
Indicates that something (such as a rule, setting, or action) is applied specifically to a particular type or category of group rather than to individual members.
-
E.
appliedToBuildingType
chosen
Indicates that something (such as a rule, measure, or classification) is specifically applicable to a particular type of building.
- 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_69e245b9c11481909d06c872214d21af |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f185192754819093a87d23371e7bbc |
completed | April 29, 2026, 4:12 a.m. |
| PD | Predicate disambiguation | batch_69ef89d5f71881908b9f9d0c8aab278c |
completed | April 27, 2026, 4:07 p.m. |
Created at: April 17, 2026, 3:54 p.m.