Triple
T3476672
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siemensstadt housing estate, Berlin |
E73391
|
entity |
| Predicate | numberOfDwellings |
P19276
|
FINISHED |
| Object | approximately 1370 |
—
|
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: approximately 1370 | Statement: [Siemensstadt housing estate, Berlin, numberOfDwellings, approximately 1370]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfDwellings Context triple: [Siemensstadt housing estate, Berlin, numberOfDwellings, approximately 1370]
-
A.
numberOfHousingUnits
chosen
Indicates the total count of distinct housing units associated with an entity or within a specified area.
-
B.
numberOfHouses
Indicates the quantity of houses associated with a given entity or context.
-
C.
hasNumberOfHouses
Indicates the quantity of houses associated with a given entity.
-
D.
numberOfBuildings
Indicates the total count of buildings associated with a given entity or within a specified context.
-
E.
hasHousingUnits
Indicates that an entity possesses or contains a specified number or set of housing units.
- 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_69ad85b3c9b08190857cae74c7f36da9 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbb5a5cb88190be5624ae224e4c91 |
completed | March 8, 2026, 6:09 p.m. |
| PD | Predicate disambiguation | batch_69adae07802c8190919c49b0e65b2797 |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:17 p.m.