Triple
T376114
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gustavo A. Madero |
E8375
|
entity |
| Predicate | hasAreaCharacteristic |
P3938
|
FINISHED |
| Object | densely populated |
—
|
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: densely populated | Statement: [Gustavo A. Madero, hasAreaCharacteristic, densely populated]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAreaCharacteristic Context triple: [Gustavo A. Madero, hasAreaCharacteristic, densely populated]
-
A.
hasAreaType
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
B.
serviceAreaCharacteristic
chosen
Indicates a relationship where a service area is associated with a specific attribute or feature that characterizes it.
-
C.
hasIconicArea
Indicates that an entity possesses a distinct, widely recognized area or region that is emblematic or characteristic of it.
-
D.
area
Indicates that one entity has a measured two-dimensional extent or surface size quantified by another entity.
-
E.
hasResidentialArea
Indicates that an entity includes, contains, or is associated with an area designated for people to live or reside.
- 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_69a2e7f2ec648190b42bc7db424f8109 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec169a848190a577aa093c878839 |
completed | Feb. 28, 2026, 1:22 p.m. |
| PD | Predicate disambiguation | batch_69a2e96351cc8190a55adf95f8c27e9e |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.