Triple
T2876473
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cranbury Township |
E56890
|
entity |
| Predicate | hasLandUseCharacter |
P43475
|
FINISHED |
| Object | suburban-rural |
—
|
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: suburban-rural | Statement: [Cranbury Township, hasLandUseCharacter, suburban-rural]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLandUseCharacter Context triple: [Cranbury Township, hasLandUseCharacter, suburban-rural]
-
A.
landUseIncludes
Indicates that a specified land area contains or permits the specified type(s) of land use within its boundaries.
-
B.
hasLandStatus
Indicates that an entity possesses a particular legal or administrative status regarding land (such as ownership, tenure, protection, or use designation).
-
C.
majorLandUse
Indicates the primary way a given area of land is utilized or designated (e.g., residential, commercial, agricultural).
-
D.
otherLandUse
Indicates that the land is used for purposes that do not fall into any of the primary or predefined land-use categories.
-
E.
primaryLandUse
Indicates the main or dominant way in which a given piece of land is utilized or designated (e.g., residential, agricultural, commercial).
- 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_69ab4a4ced288190ab6d3e062d10f7f6 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abe0061d048190bb1e5a01e7ceb0e2 |
completed | March 7, 2026, 8:21 a.m. |
| PD | Predicate disambiguation | batch_69abdd142e4c8190b424cb0c5ff40d04 |
completed | March 7, 2026, 8:08 a.m. |
| PDg | Predicate description generation | batch_69abde2cdcc48190827195d3ae70aa19 |
completed | March 7, 2026, 8:13 a.m. |
Created at: March 6, 2026, 10:03 p.m.