Triple
T9194703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dalworthington Gardens, Texas |
E220673
|
entity |
| Predicate | landAreaCharacteristic |
P86936
|
FINISHED |
| Object | small geographic 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: small geographic area | Statement: [Dalworthington Gardens, Texas, landAreaCharacteristic, small geographic area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: landAreaCharacteristic Context triple: [Dalworthington Gardens, Texas, landAreaCharacteristic, small geographic area]
-
A.
landArea
Indicates the total surface area of a piece of land associated with an entity, typically measured in standardized units (e.g., square meters, hectares).
-
B.
acquiredLandArea
Indicates the total area of land that has been obtained or taken possession of through an acquisition.
-
C.
landAreaSquareMiles
Indicates the size of a geographic area measured in square miles.
-
D.
landAreaShare
Indicates the proportion of a larger geographic area’s total land area that is accounted for by a specific sub-area or unit.
-
E.
coreAreaCharacteristic
Indicates that a characteristic or feature is specifically associated with the core area of something, rather than its peripheral or secondary parts.
- 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_69ca83e7ba70819088b74866d9da2c30 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccd87c3a3c8190b60f19873ef6e1f8 |
completed | April 1, 2026, 8:34 a.m. |
| PD | Predicate disambiguation | batch_69cc660af2408190ae06eb8326e1c64e |
completed | April 1, 2026, 12:25 a.m. |
| PDg | Predicate description generation | batch_69cc6745c17881908df72443e0800068 |
completed | April 1, 2026, 12:31 a.m. |
Created at: March 30, 2026, 7:25 p.m.