Triple
T1954345
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Wharf (Washington, D.C.) |
E42230
|
entity |
| Predicate | numberOfResidentialUnits |
P19276
|
FINISHED |
| Object | over 1,000 |
—
|
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 1,000 | Statement: [The Wharf (Washington, D.C.), numberOfResidentialUnits, over 1,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfResidentialUnits Context triple: [The Wharf (Washington, D.C.), numberOfResidentialUnits, over 1,000]
-
A.
numberOfHousingUnits
chosen
Indicates the total count of distinct housing units associated with an entity or within a specified area.
-
B.
hasHousingUnits
Indicates that an entity possesses or contains a specified number or set of housing units.
-
C.
numberOfHouses
Indicates the quantity of houses associated with a given entity or context.
-
D.
numberOfBedrooms
Indicates the quantity of bedrooms associated with a given property or dwelling.
-
E.
numberOfBuildings
Indicates the total count of buildings associated with a given entity or within a specified context.
- 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_69a8870eea088190a38781990812a9bc |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb351c148819080173c09876e814a |
completed | March 7, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_69abaff3eda88190b643994cb4dfb8df |
completed | March 7, 2026, 4:56 a.m. |
Created at: March 4, 2026, 7:36 p.m.