Triple
T7851213
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Plata, Maryland |
E182056
|
entity |
| Predicate | distanceFromWashingtonDCInMiles |
P15076
|
FINISHED |
| Object | about 30 |
—
|
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: about 30 | Statement: [La Plata, Maryland, distanceFromWashingtonDCInMiles, about 30]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromWashingtonDCInMiles Context triple: [La Plata, Maryland, distanceFromWashingtonDCInMiles, about 30]
-
A.
distanceToWashingtonDC
chosen
Indicates the physical distance between a given location and Washington, D.C.
-
B.
distanceToBaltimoreInMiles
Indicates the numerical distance, measured in miles, between a given location and the city of Baltimore.
-
C.
distanceToWashingtonMonument
Indicates the physical distance between a given entity’s location and the Washington Monument.
-
D.
distanceFromFord’sTheatre
Indicates the spatial distance between an entity and Ford’s Theatre.
-
E.
distanceFromMiami
Indicates the spatial distance between a given entity’s location and the city of Miami.
- 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_69ca82869ee08190b8f9040dbc2c0467 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb18eaac508190bf373b1d50b52e1e |
completed | March 31, 2026, 12:44 a.m. |
| PD | Predicate disambiguation | batch_69cae92180f88190ae3d44c3de7adc93 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 4:50 p.m.