Triple
T9790673
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santhome |
E237596
|
entity |
| Predicate | locatedInUrbanAreaType |
P66935
|
FINISHED |
| Object | residential and institutional area |
—
|
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: residential and institutional area | Statement: [Santhome, locatedInUrbanAreaType, residential and institutional area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locatedInUrbanAreaType Context triple: [Santhome, locatedInUrbanAreaType, residential and institutional area]
-
A.
locatedInUrbanizationType
chosen
Indicates that one entity is situated within, or belongs to, a specific type or category of urbanized area (e.g., city, suburb, metropolitan zone).
-
B.
withinUrbanArea
Indicates that one entity is located inside the spatial boundaries of an urban area associated with another entity.
-
C.
containsUrbanArea
Indicates that a geographic region fully or partially encompasses an urbanized area within its boundaries.
-
D.
hasUrbanAreaApprox
Indicates an approximate measure or estimate of the size or extent of an entity’s urban area.
-
E.
appliesToUrbanArea
Indicates that the relationship, rule, or condition is specifically relevant or applicable to an urban area.
- 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_69ca84dc04488190b9c91193976c0960 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda215b3108190a897552e1dc91cc4 |
completed | April 1, 2026, 10:54 p.m. |
| PD | Predicate disambiguation | batch_69cd03d77c6c81909b675955bf113320 |
completed | April 1, 2026, 11:39 a.m. |
Created at: March 30, 2026, 8:28 p.m.