Triple
T1180199
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lynnfield |
E25116
|
entity |
| Predicate | hasLandUseType |
P14072
|
FINISHED |
| Object | suburban residential |
—
|
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 residential | Statement: [Lynnfield, hasLandUseType, suburban residential]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLandUseType Context triple: [Lynnfield, hasLandUseType, suburban residential]
-
A.
primaryLandUse
chosen
Indicates the main or dominant way in which a given piece of land is utilized or designated (e.g., residential, agricultural, commercial).
-
B.
otherLandUse
Indicates that the land is used for purposes that do not fall into any of the primary or predefined land-use categories.
-
C.
hasAreaType
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
D.
hasNearbyLandUse
Indicates that one land area is located close to another area characterized by a specific type of land use.
-
E.
secondaryLandUse
Indicates a secondary or additional way in which a piece of land is used, beyond its primary designated use.
- 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_69a494267b4c819088c97a59182bf56a |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd53e4b48190abb2167f8074a6bc |
completed | March 1, 2026, 10:27 p.m. |
| PD | Predicate disambiguation | batch_69a4bb5844348190b01ac6506906ba3b |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:45 p.m.