Triple
T2227
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York City |
E40
|
entity |
| Predicate | demographicsCharacteristic |
P299
|
FINISHED |
| Object | high population density |
—
|
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: high population density | Statement: [New York City, demographicsCharacteristic, high population density]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: demographicsCharacteristic Context triple: [New York City, demographicsCharacteristic, high population density]
-
A.
demographicsNote
Indicates that there is an associated note or commentary describing demographic-related information about an entity.
-
B.
hasDemonym
Indicates that one entity is the term (demonym) used to refer to the inhabitants or natives of another entity (typically a place).
-
C.
ethnicOrigin
Indicates the relationship where an entity is associated with a particular ethnic group or ancestry.
-
D.
describes
Indicates that one entity provides an explanation, representation, or account of another entity or concept.
-
E.
sexOrGender
Indicates that one entity has a specified biological sex or socially constructed gender identity.
- 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_69a22cde80848190b62c5f556b4d62ba |
completed | Feb. 27, 2026, 11:46 p.m. |
| NER | Named-entity recognition | batch_69a23344daf8819083118bbac5f46568 |
completed | Feb. 28, 2026, 12:13 a.m. |
| PD | Predicate disambiguation | batch_69a232e52e7c81909c072703e28e8c61 |
completed | Feb. 28, 2026, 12:12 a.m. |
| PDg | Predicate description generation | batch_69a233443224819097b91b150fdfbd1a |
completed | Feb. 28, 2026, 12:13 a.m. |
Created at: Feb. 27, 2026, 11:48 p.m.