Triple
T33943568
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Indian Point Energy Center |
E870230
|
entity |
| Predicate | distanceToNewYorkCity_km |
P51356
|
FINISHED |
| Object | approximately 60 |
—
|
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: approximately 60 | Statement: [Indian Point Energy Center, distanceToNewYorkCity_km, approximately 60]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToNewYorkCity_km Context triple: [Indian Point Energy Center, distanceToNewYorkCity_km, approximately 60]
-
A.
distanceToNewYorkCityApproxKm
chosen
Indicates the approximate physical distance, measured in kilometers, between a given entity’s location and New York City.
-
B.
distanceToNewYorkCity
Indicates the spatial distance between a given entity’s location and New York City.
-
C.
distanceFromBeijing_km
Indicates the physical distance, measured in kilometers, between a given place or object and Beijing.
-
D.
tourDistanceApproxKm
Indicates an approximate total distance, measured in kilometers, covered during a tour or journey.
-
E.
distanceFromTokyo
Indicates the physical distance between a given location and Tokyo.
- 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_69f3499b0dd48190b07b4b60babcee02 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a037e0953908190b2930b3c06a40129 |
completed | May 12, 2026, 7:22 p.m. |
| PD | Predicate disambiguation | batch_6a0379f6c3308190b954f7810214ceed |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 1:49 a.m.