Triple
T22358982
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Palmanova |
E552728
|
entity |
| Predicate | distanceTo Palma de Mallorca (approximate km) |
P147896
|
FINISHED |
| Object | about 15 |
—
|
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 15 | Statement: [Palmanova, distanceTo Palma de Mallorca (approximate km), about 15]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceTo Palma de Mallorca (approximate km) Context triple: [Palmanova, distanceTo Palma de Mallorca (approximate km), about 15]
-
A.
distanceToIbiza
Indicates the spatial distance between a given entity’s location and the location of Ibiza.
-
B.
distanceToMálaga
Indicates the spatial distance between a given entity and the location of Málaga.
-
C.
distanceFromSeville
Indicates the spatial distance separating an entity from the location of Seville.
-
D.
distanceToBerlengas
Indicates the measured distance between a given entity’s location and the Berlengas.
-
E.
distanceFromSantaCruzDeTenerifeKm
Indicates the distance, measured in kilometers, between an entity and Santa Cruz de Tenerife.
- 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_69e11e4affcc8190ba7c27d29062558d |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f157d2b9fc81909e09a1c48664b895 |
completed | April 29, 2026, 12:58 a.m. |
| PD | Predicate disambiguation | batch_69e7300c20088190a59e5bf9e70384f3 |
completed | April 21, 2026, 8:06 a.m. |
| PDg | Predicate description generation | batch_69e7342ce08c8190bc0a7085f4a952e7 |
completed | April 21, 2026, 8:24 a.m. |
Created at: April 16, 2026, 8:44 p.m.