Triple
T3497290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Almuñécar |
E73880
|
entity |
| Predicate | distanceToGranada |
P48751
|
FINISHED |
| Object | approximately 80 km |
—
|
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 80 km | Statement: [Almuñécar, distanceToGranada, approximately 80 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToGranada Context triple: [Almuñécar, distanceToGranada, approximately 80 km]
-
A.
distanceToMadrid
Indicates the physical distance between a given location or entity and the city of Madrid.
-
B.
distanceFromZagora
Indicates the spatial distance between a given entity and the location named Zagora.
-
C.
distanceFromSantaCruzDeTenerifeKm
Indicates the distance, measured in kilometers, between an entity and Santa Cruz de Tenerife.
-
D.
distanceToSanDiego
Indicates the measured or estimated distance between a given entity’s location and the city of San Diego.
-
E.
distanceToSantaMarta
Indicates the measured spatial distance between a given entity’s location and the location of Santa Marta.
- 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_69ad85cdb6e48190a335d412b9194ed8 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbbd16c0081908f13535f459618d1 |
completed | March 8, 2026, 6:11 p.m. |
| PD | Predicate disambiguation | batch_69adae0b34908190b2bb5766a2231f7a |
completed | March 8, 2026, 5:12 p.m. |
| PDg | Predicate description generation | batch_69adb055ed2481908171effe28151cf9 |
completed | March 8, 2026, 5:22 p.m. |
Created at: March 8, 2026, 3:18 p.m.