Triple
T6636636
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | División de Reserva |
E150470
|
entity |
| Predicate | hasEquipmentStatus |
P71865
|
FINISHED |
| Object | medios asignados a la reserva |
—
|
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: medios asignados a la reserva | Statement: [División de Reserva, hasEquipmentStatus, medios asignados a la reserva]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEquipmentStatus Context triple: [División de Reserva, hasEquipmentStatus, medios asignados a la reserva]
-
A.
hasModelStatus
Indicates that an entity is assigned a particular model-related state or condition, such as its current phase, validity, or operational status within a modeling context.
-
B.
hasUnitStatus
Indicates that an entity is associated with a particular operational or condition status as a unit.
-
C.
operatorStatus
Indicates the current operational state or condition of an operator (e.g., active, inactive, or in error).
-
D.
availabilityStatus
Indicates the current state of whether something is obtainable, usable, or accessible at a given time.
-
E.
hasOccupancyStatus
Indicates the current usage or availability state of something, such as whether it is occupied, vacant, or otherwise in use.
- 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_69c687f0ceb08190bf40807bfc605fa5 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6c308a08881908501c862b3029321 |
completed | March 27, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69c6ad024860819084b9b535b136ede6 |
completed | March 27, 2026, 4:14 p.m. |
| PDg | Predicate description generation | batch_69c6c30733908190980f7ffaa5c5527b |
completed | March 27, 2026, 5:48 p.m. |
Created at: March 27, 2026, 1:59 p.m.