Triple
T6309267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Florence American Cemetery |
E141458
|
entity |
| Predicate | distanceFromFlorence |
P69982
|
FINISHED |
| Object | about 12 kilometers south |
—
|
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 12 kilometers south | Statement: [Florence American Cemetery, distanceFromFlorence, about 12 kilometers south]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromFlorence Context triple: [Florence American Cemetery, distanceFromFlorence, about 12 kilometers south]
-
A.
distanceFromRome
Indicates the measured spatial distance between a given entity’s location and the city of Rome.
-
B.
distanceToRome
Indicates the spatial distance between a given entity and the location of Rome.
-
C.
distanceFromMilan
Indicates the spatial distance between a given entity and the city of Milan.
-
D.
distanceToSavona_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Savona.
-
E.
distanceToGenoa_km
Indicates the physical distance, measured in kilometers, between a given place or object and the city of Genoa.
- 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_69c008d00efc8190a36c05b4b4a3bf4b |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0647f13a4819095c4ce8c42c5d1fb |
completed | March 22, 2026, 9:51 p.m. |
| PD | Predicate disambiguation | batch_69c060e311b48190b1c74a5cf9435623 |
completed | March 22, 2026, 9:36 p.m. |
| PDg | Predicate description generation | batch_69c0623bb29081908bfdfb84a07ece90 |
completed | March 22, 2026, 9:42 p.m. |
Created at: March 22, 2026, 4:28 p.m.