Triple
T3736030
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Belmont |
E79185
|
entity |
| Predicate | hasProximityFeature |
P28961
|
FINISHED |
| Object | large urban park |
—
|
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: large urban park | Statement: [Belmont, hasProximityFeature, large urban park]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProximityFeature Context triple: [Belmont, hasProximityFeature, large urban park]
-
A.
hasNearbyMode
Indicates that one entity has another entity located close enough to be considered in its immediate vicinity or surrounding area.
-
B.
hasNFC
Indicates that one entity possesses or supports Near Field Communication (NFC) capability in relation to another entity or context.
-
C.
hasNearbyFunction
chosen
Indicates that one entity has another entity located close by that serves a related or supportive function.
-
D.
hasNearDetectorFunction
Indicates that one entity functions as a near (close-range or proximal) detector for another entity.
-
E.
nearPass
Indicates that one entity moves or travels close to another entity without necessarily making direct contact or interaction.
- 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_69ad8b0e4650819090ad7cef094285e8 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcb3b399c819091b42209925c0d8f |
completed | March 8, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_69adc04746588190b0dc535638f23546 |
completed | March 8, 2026, 6:30 p.m. |
Created at: March 8, 2026, 3:34 p.m.