Triple
T8929814
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martorell Technical Center |
E212622
|
entity |
| Predicate | typeOfRAndDFacility |
P2836
|
FINISHED |
| Object | automotive engineering center |
—
|
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: automotive engineering center | Statement: [Martorell Technical Center, typeOfRAndDFacility, automotive engineering center]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfRAndDFacility Context triple: [Martorell Technical Center, typeOfRAndDFacility, automotive engineering center]
-
A.
designedFacilityType
Indicates the type or category of facility that something (such as a plan, system, or component) is specifically designed for.
-
B.
productionFacilityType
Indicates the specific kind or category of facility where production or manufacturing activities take place.
-
C.
hasFacilityType
chosen
Indicates that an entity possesses or is associated with a specific type or category of facility.
-
D.
campusFacilityType
Indicates the specific kind of facility a campus location is classified as (e.g., library, laboratory, residence hall).
-
E.
typeOfPortFacility
Indicates that one port facility is classified as a specific type or category of port facility in relation to another.
- 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_69ca8395c438819087d7cb844ab5990c |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6676d5d881908ce78cbb5561a68b |
completed | April 1, 2026, 12:27 a.m. |
| PD | Predicate disambiguation | batch_69cc5ed3286c8190a21de2ee11f2639f |
completed | March 31, 2026, 11:54 p.m. |
Created at: March 30, 2026, 6:57 p.m.