Triple
T10632487
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Tower |
E250491
|
entity |
| Predicate | numberOfResidentialApartments |
P19276
|
FINISHED |
| Object | over 400 |
—
|
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: over 400 | Statement: [North Tower, numberOfResidentialApartments, over 400]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfResidentialApartments Context triple: [North Tower, numberOfResidentialApartments, over 400]
-
A.
numberOfResidentialFloors
Indicates the total count of floors in a building that are designated for residential use.
-
B.
numberOfHousingUnits
chosen
Indicates the total count of distinct housing units associated with an entity or within a specified area.
-
C.
hasResidentialFloors
Indicates that an entity (such as a building or structure) includes one or more floors designated for residential use.
-
D.
approximateNumberOfRooms
Indicates an estimated or not precisely known count of rooms associated with an entity.
-
E.
numberOfBedrooms
Indicates the quantity of bedrooms associated with a given property or dwelling.
- 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_69d6aa5993448190a493b790b8f85010 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6df95f5e88190b34ce3ec972759ef |
completed | April 8, 2026, 11:07 p.m. |
| PD | Predicate disambiguation | batch_69d6dd83b114819098e84dc658e82d7e |
completed | April 8, 2026, 10:58 p.m. |
Created at: April 8, 2026, 9:02 p.m.