Triple
T4295671
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metropolitan Opera House |
E99704
|
entity |
| Predicate | numberOfBalconies |
P55295
|
FINISHED |
| Object | multiple tiers |
—
|
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: multiple tiers | Statement: [Metropolitan Opera House, numberOfBalconies, multiple tiers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfBalconies Context triple: [Metropolitan Opera House, numberOfBalconies, multiple tiers]
-
A.
hasBalcony
Indicates that a building or dwelling includes a balcony as part of its structure or features.
-
B.
hasBalconyUse
Indicates that an entity makes use of or has access to a balcony for some purpose or activity.
-
C.
numberOfBathrooms
Indicates the total count of bathrooms associated with an entity (such as a property or unit).
-
D.
hasBalustrades
Indicates that one entity features or is equipped with balustrades as part of its structure or design.
-
E.
numberOfFloors
Indicates the total count of distinct floor levels that a building or structure has.
- 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_69b3455175088190aa79c6e03b86647e |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35085b864819086cf726285384566 |
completed | March 12, 2026, 11:47 p.m. |
| PD | Predicate disambiguation | batch_69b347fe55a88190b77bab0c0f38e1aa |
completed | March 12, 2026, 11:10 p.m. |
| PDg | Predicate description generation | batch_69b34e0606488190baadf469a1afc3c2 |
completed | March 12, 2026, 11:36 p.m. |
Created at: March 12, 2026, 11:08 p.m.